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人工智能生态相关的专业名词解释
人工智能生态相关的专业名词解释下载温馨提示：该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。
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我想了解的一项事物英语作文
我想了解的一项事物英语作文There are countless things in this world that pique my curiosity and interest. From the intricacies of the natural world to the complexities of human society, there is always more to discover and comprehend. However, one particular subject that I am deeply intrigued by and wish to understand more fully is the field of artificial intelligence.The rapid advancements in AI technology over the past few decades have been truly astounding. The ability of machines to process vast amounts of data, recognize patterns, and make autonomous decisions has transformed countless industries and revolutionized the way we live our lives. From virtual assistants that can understand and respond to natural language, to self-driving cars that navigate the roads with precision, the applications of AI are seemingly limitless.What fascinates me most about AI is the sheer potential it holds to reshape our world and enhance the human experience. As the technology continues to evolve, the possibilities for how it can be leveraged to solve complex problems, streamline processes, andaugment human capabilities are truly exciting. Imagine a future where AI systems can assist in medical diagnoses, optimize energy grids, or even help us explore the depths of space. The potential applications are staggering.At the same time, the rapid advancement of AI also raises a number of ethical and philosophical questions that I am eager to explore. As AI systems become more sophisticated and autonomous, how do we ensure they are aligned with human values and interests? How do we address concerns about job displacement, privacy, and the potential for misuse? What are the implications for the nature of consciousness, intelligence, and what it means to be human?These are the kinds of questions that I find deeply compelling and that I believe are crucial to grapple with as we continue to develop and deploy AI technologies. I am fascinated by the philosophical and ethical debates surrounding AI, and I am eager to delve deeper into the technical and scientific aspects of the field as well.One particular area of AI that I am particularly intrigued by is the concept of machine learning. The ability of machines to learn and adapt based on data is a truly remarkable feat, and I am eager to understand the underlying algorithms and mathematical principles that enable this capability. How do these systems identify patterns, make predictions, and continuously improve their performance?What are the limitations and potential pitfalls of machine learning, and how can we ensure these systems are robust and reliable?Additionally, I am deeply curious about the potential for AI to enhance and augment human intelligence. As AI systems become more sophisticated, there may be opportunities for seamless collaboration between humans and machines, where our respective strengths and capabilities are leveraged in complementary ways. How might this change the nature of work, education, and even our daily lives? What are the implications for the future of human creativity, problem-solving, and decision-making?These are the kinds of questions that I am eager to explore further through my own research and study. I believe that a deeper understanding of AI and its implications will not only satisfy my own intellectual curiosity, but also equip me with the knowledge and tools to navigate the rapidly changing technological landscape of the 21st century.To that end, I am committed to pursuing a rigorous and multidisciplinary approach to learning about AI. This will involve delving into the technical and scientific aspects of the field, as well as exploring the philosophical, ethical, and societal implications. I plan to take courses in computer science, mathematics, and cognitive science, while also engaging with the growing body of literature onthe ethical and social dimensions of AI.Additionally, I am eager to engage with the broader AI community, both online and in person. Attending conferences, participating in hackathons, and collaborating with researchers and practitioners in the field will provide me with valuable opportunities to learn, network, and contribute to the ongoing dialogue around AI.Ultimately, my goal is to develop a deep and nuanced understanding of AI that will not only satisfy my own intellectual curiosity, but also equip me with the knowledge and skills to play a meaningful role in shaping the future of this transformative technology. Whether it is through a career in AI research, policy development, or ethical oversight, I am committed to using my knowledge and expertise to ensure that AI is developed and deployed in a way that enhances and empowers humanity, rather than undermining our values and well-being.In conclusion, the field of artificial intelligence is a vast and multifaceted subject that I am deeply passionate about exploring. From the technical intricacies of machine learning to the profound philosophical and ethical implications of AI, there is always more to discover and understand. By pursuing a comprehensive and interdisciplinary approach to learning about AI, I am confident that I can develop the knowledge and skills necessary to navigate therapidly changing technological landscape of the 21st century and contribute to the responsible development and deployment of this transformative technology.。
模式识别与机器学习 复习资料 温雯 老师
温雯
一些需要提及的问题
温雯 广东工业大学 计算机学院 23
温雯
广东工业大学
计算机学院
21
温雯
广东工业大学
模式识别系统的复杂性 – An Example
“利用光学传感器采集信息，对 传送带上的鱼进行种类的自动 区分” Fish Classification: Sea Bass / Salmon
一个例子
将鲈鱼与三文鱼进行区分 问题归纳（抽象而言） • 模式识别系统 • 设计流程
Preprocessing involves:
广东工业大学 计算机学院 28
Overlap in the histograms is small compared to length feature 温雯 广东工业大学 计算机学院
27
温雯
判定边界
错误分类的代价
模型的复杂度
Generalization (推广能力）
Partition the feature space into two regions by finding the decision boundary （判定边界）that minimizes the error.
Optical Character Recognition (typography)
A v t u I h D U w K
一种新的人机交互系统 你，从中看到模式识别吗？
Vision
机器学习与人工智能领域中常用的英语词汇
机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。
人工智能的核心知识点
人工智能的核心知识点人工智能（Artificial Intelligence，简称AI）作为一门前沿的技术和学科，涵盖了广泛的领域和知识点。
本文将着重介绍人工智能的核心知识点，深入探讨其关键概念和应用领域。
一、机器学习（Machine Learning）机器学习是人工智能的核心方法论之一，它通过计算机系统自动学习和改进，从经验数据中提取知识，并基于这些知识进行预测和决策。
机器学习包括监督学习、无监督学习和强化学习等不同类型的学习算法。
监督学习通过输入训练数据和对应的标签，让机器学习预测输出结果，并根据预测结果和实际标签之间的差异来优化模型。
无监督学习则不需要标签，通过自动发现数据集中的模式和结构来进行学习。
强化学习则通过与环境的交互，根据行为的反馈来学习制定最佳的策略。
二、深度学习（Deep Learning）深度学习是机器学习领域的一个重要分支，模拟了人脑神经网络的结构和工作原理，通过多层次的神经网络模型来处理和学习数据。
深度学习在语音识别、图像识别、自然语言处理等领域取得了显著的成果。
其中，卷积神经网络（Convolutional Neural Network，简称CNN）适用于图像和视频处理任务，循环神经网络（Recurrent Neural Network，简称RNN）适用于序列数据和自然语言处理任务。
深度学习的关键技术包括激活函数、损失函数、优化算法等。
三、自然语言处理（Natural Language Processing，简称NLP）自然语言处理是研究计算机如何理解和处理人类语言的技术领域。
NLP主要包括文本分析、文本生成、情感分析和机器翻译等任务。
在自然语言处理中，词嵌入（Word Embedding）是常用的技术，它将文本中的单词映射为高维向量，以便计算机能够理解和处理。
另外，机器翻译也是NLP的重要应用，通过将一种语言的文本翻译为另一种语言，实现跨语言的交流和沟通。
四、计算机视觉（Computer Vision）计算机视觉是研究计算机如何理解和处理图像和视频的技术领域。
研究NLP100篇必读的论文---已整理可直接下载
研究NLP100篇必读的论⽂---已整理可直接下载100篇必读的NLP论⽂⾃⼰汇总的论⽂集，已更新链接：提取码：x7tnThis is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read.这是100篇重要的⾃然语⾔处理(NLP)论⽂的列表，认真的学⽣和研究⼈员在这个领域应该知道和阅读。
This list is compiled by .本榜单由编制。
I welcome any feedback on this list. 我欢迎对这个列表的任何反馈。
This list is originally based on the answers for a Quora question I posted years ago: .这个列表最初是基于我多年前在Quora上发布的⼀个问题的答案:[所有NLP学⽣都应该阅读的最重要的研究论⽂是什么?]( -are-the-most-important-research-paper -which-all-NLP-students-should- definitread)。
I thank all the people who contributed to the original post. 我感谢所有为原创⽂章做出贡献的⼈。
This list is far from complete or objective, and is evolving, as important papers are being published year after year.由于重要的论⽂年复⼀年地发表，这份清单还远远不够完整和客观，⽽且还在不断发展。
复杂网络演化博弈理论研究综述
复杂网络演化博弈理论研究综述一、本文概述Overview of this article随着信息技术的飞速发展，复杂网络作为一种描述现实世界中各种复杂系统的有效工具，已经引起了广泛关注。
而在复杂网络中，演化博弈理论则为我们提供了一种深入理解和分析网络动态行为的重要视角。
本文旨在全面综述复杂网络演化博弈理论的研究现状和发展趋势，以期能为相关领域的学者和研究人员提供有益的参考和启示。
With the rapid development of information technology, complex networks have attracted widespread attention as an effective tool for describing various complex systems in the real world. In complex networks, evolutionary game theory provides us with an important perspective to deeply understand and analyze the dynamic behavior of networks. This article aims to comprehensively review the research status and development trends of complex network evolutionary game theory, in order to provide useful reference and inspiration for scholars and researchers in related fields.本文首先回顾了复杂网络和演化博弈理论的基本概念和研究背景，阐述了两者结合的必要性和重要性。
接着，文章从网络结构、博弈规则、动态演化等多个方面对复杂网络演化博弈理论进行了深入的分析和讨论。
基于对象特征的深度哈希跨模态检索
基于对象特征的深度哈希跨模态检索朱杰1，白弘煜1，张仲羽1，谢博鋆2，张俊三3+1.中央司法警官学院信息管理系，河北保定0710002.河北大学数学与信息科学学院，河北保定0710023.中国石油大学（华东）计算机科学与技术学院，山东青岛266580+通信作者E-mail:*******************.cn 摘要：随着不同模态的数据在互联网中的飞速增长，跨模态检索逐渐成为了当今的一个热点研究问题。
哈希检索因其快速、有效的特点，成为了大规模数据跨模态检索的主要方法之一。
在众多图像-文本的深度跨模态检索算法中，设计的准则多为尽量使得图像的深度特征与对应文本的深度特征相似。
但是此类方法将图像中的背景信息融入到特征学习中，降低了检索性能。
为了解决此问题，提出了一种基于对象特征的深度哈希（OFBDH ）跨模态检索方法。
此方法从特征映射中学习到优化的、有判别力的极大激活特征作为对象特征，并将其融入到图像与文本的跨模态网络学习中。
实验结果表明，OFBDH 能够在MIRFLICKR-25K 、IAPR TC-12和NUS-WIDE 三个数据集上获得良好的跨模态检索结果。
关键词：对象特征；跨模态损失；网络参数学习；检索文献标志码：A中图分类号：TP391Object Feature Based Deep Hashing for Cross-Modal RetrievalZHU Jie 1,BAI Hongyu 1,ZHANG Zhongyu 1,XIE Bojun 2,ZHANG Junsan 3+1.Department of Information Management,The National Police University for Criminal Justice,Baoding,Hebei 071000,China2.College of Mathematics and Information Science,Hebei University,Baoding,Hebei 071002,China3.College of Computer Science and Technology,China University of Petroleum,Qingdao,Shandong 266580,China Abstract:With the rapid growth of data with different modalities on the Internet,cross-modal retrieval has gradually become a hot research topic.Due to its efficiency and effectiveness,Hashing based methods have become one of the most popular large-scale cross-modal retrieval strategies.In most of the image-text cross-modal retrieval methods,the goal is to make the deep features of the images similar to the corresponding deep text features.However,these methods incorporate background information of the images into the feature learning,as a result,the retrieval performance is decreased.To solve this problem,OFBDH (object feature based deep Hashing)is proposed to learn计算机科学与探索1673-9418/2021/15(05)-0922-09doi:10.3778/j.issn.1673-9418.2006062基金项目：国家自然科学基金（61802269）；河北省自然科学基金青年基金项目（F2018511002）；河北省高等学校科学技术研究项目（Z2019037,QN2018251）；河北大学高层次创新人才科研启动经费项目；2019年中央司法警官学院省级大学生创新创业训练计划项目（S201911903004）。
成为聪明的网络学者英语作文高中
成为聪明的网络学者英语作文高中Becoming a Smart Internet ScholarHi there! My name is Jacob and I'm 12 years old. I love using the internet and learning new things online. But I've also learned that you have to be really careful and smart when you're on the internet. There's a lot of cool stuff out there, but also some things that aren't so great. Today, I want to share some tips with you on how to become a smart and safe internet scholar!The first thing you need to understand is that not everything you read online is true. There's a ton of misinformation and fake news out there. You have to be like a detective and check the sources to see if the information is coming from a reliable place or not. I always ask myself - who wrote this? Is it from a trusted website like a university, government, or well-known newspaper? Or is it just some random person's opinion on a blog? That's an important first step.Another thing to watch out for is bias. Lots of websites and articles have an agenda or a certain viewpoint they want to push. They might leave out key facts or twist things to make their side look better. A smart internet scholar needs to read stuff from multiple perspectives to get the full picture. Don't just readthings you agree with - read opinions you disagree with too and really think critically about them.Speaking of critical thinking, that's like the superpower you need to master the internet. You can't just believe everything you read. You have to really analyze the information, look for holes in the arguments, cross-check facts, and come to your own conclusions. Thinking for yourself is crucial online.As you're doing research, make sure you're using good keywords and search strategies to find the best and most reliable sources. Typing things into Google is just the start - you have to dig deeper into the results, read multiple articles, and piece it all together. Taking good notes and organizing your research is also key.When it comes to citing your sources, you have to be super careful to give proper credit where it's due. Plagiarism - taking someone else's work and claiming it as your own - is a hugeno-no in academics. Make sure you quote and cite everything correctly so you don't get in trouble. There are formats like MLA and APA that give you the rules for how to cite different types of sources properly.Another huge part of being an internet scholar is learning how to communicate and share information effectively online. You have to think about stuff like:• Writing clear emails• Commenting constructively on blogs/forums• Making good PowerPoint presentations• Collaborating on group projects online• Creating quality social media postsYou're not just consuming info anymore - you're adding your own voice and becoming a creator yourself. That means really working on your writing, media literacy, and digital citizenship skills.Building a positive digital footprint is something else smart internet scholars have to think about. The stuff you post and do online can stick around forever, so you have to be mindful of the trail you're leaving behind. Future colleges and employers may look at that someday!Finally, don't forget that too much time staring at screens isn't healthy. You've got to find a good balance between using the internet as a learning tool and making sure you get outside,exercise, spend time with friends and family, read physical books, and everything else. Moderation is key for smart internet scholars!Well, those are some of my tips for becoming an awesome, responsible, and savvy internet researcher. It's a powerful skill that can help you learn about any topic that interests you. Just make sure you do it the smart way - by verifying sources, analyzing information critically, citing properly, communicating thoughtfully, and staying safe. The internet can be an amazing educational resource when you use it right. What are you waiting for? Start becoming an internet scholar today!。
参考文献(人工智能)
参考文献(人工智能)曹晖目的：对参考文献整理（包括摘要、读书笔记等），方便以后的使用。
分类：粗分为论文（paper）、教程（tutorial）和文摘（digest）。
0介绍 (1)1系统与综述 (1)2神经网络 (2)3机器学习 (2)3.1联合训练的有效性和可用性分析 (2)3.2文本学习工作的引导 (2)3.3★采用机器学习技术来构造受限领域搜索引擎 (3)3.4联合训练来合并标识数据与未标识数据 (5)3.5在超文本学习中应用统计和关系方法 (5)3.6在关系领域发现测试集合规律性 (6)3.7网页挖掘的一阶学习 (6)3.8从多语种文本数据库中学习单语种语言模型 (6)3.9从因特网中学习以构造知识库 (7)3.10未标识数据在有指导学习中的角色 (8)3.11使用增强学习来有效爬行网页 (8)3.12★文本学习和相关智能A GENTS：综述 (9)3.13★新事件检测和跟踪的学习方法 (15)3.14★信息检索中的机器学习——神经网络，符号学习和遗传算法 (15)3.15用NLP来对用户特征进行机器学习 (15)4模式识别 (16)4.1JA VA中的模式处理 (16)0介绍1系统与综述2神经网络3机器学习3.1 联合训练的有效性和可用性分析标题：Analyzing the Effectiveness and Applicability of Co-training链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Analyzing the Effectiveness and Applicability of Co-training.ps作者：Kamal Nigam, Rayid Ghani备注：Kamal Nigam (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, knigam@)Rayid Ghani (School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 rayid@)摘要：Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies todatasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminativein nature and robust to the assumptions of their embedded classifiers.3.2 文本学习工作的引导标题：Bootstrapping for Text Learning Tasks链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Bootstrap for Text Learning Tasks.ps作者：Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff备注：Rosie Jones (rosie@, 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213)Andrew McCallum (mccallum@, 2 Just Research, 4616 Henry Street, Pittsburgh, PA 15213)Kamal Nigam (knigam@)Ellen Riloff (riloff@, Department of Computer Science, University of Utah, Salt Lake City, UT 84112)摘要：When applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and alarge collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; it then iterates, applying the learner to calculate labels for the unlabeled data, and incorporating some of these labels into the training input for the learner. Two case studies of this approach are presented. Bootstrapping for information extraction provides 76% precision for a 250-word dictionary for extracting locations from web pages, when starting with just a few seed locations. Bootstrapping a text classifier from a few keywords per class and a class hierarchy provides accuracy of 66%, a level close to human agreement, when placing computer science research papers into a topic hierarchy. The success of these two examples argues for the strength of the general bootstrapping approach for text learning tasks.3.3 ★采用机器学习技术来构造受限领域搜索引擎标题：Building Domain-specific Search Engines with Machine Learning Techniques链接：Papers 论文集\AI 人工智能\Machine Learning 机器学习\Building Domain-Specific Search Engines with Machine Learning Techniques.ps作者：Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore备注：Andrew McCallum (mccallum@ , Just Research, 4616 Henry Street Pittsburgh, PA 15213)Kamal Nigam (knigam@ , School of Computer Science, Carnegie Mellon University Pittsburgh, PA 15213)Jason Rennie (jr6b@)Kristie Seymore (kseymore@)摘要：Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, allows complex queries by age-group, size, location and cost over summer camps. Unfortunately these domain-specific search engines are difficult and time-consuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine forcomputer science research papers. It already contains over 50,000 papers and is publicly available at ....采用多项Naive Bayes 文本分类模型。
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its learned knowledge. It also o ers other useful functions. The user can leave WebWatcher at any time by telling the system whether the search was successful or not. WebWatcher o ers the following functionality: 1. highlighting hyperlinks on the current page, which WebWatcher deems useful according to the user's stated interests 2. adding new hyperlinks to the current page, based on the user's interests 3. suggesting pages related to the current page 4. sending email messages to the user whenever speci ed pages change Figures 1 to 5 illustrate the sequence of web pages a user visits in a typical example. Figure 1 shows an HTML-page about machine learning in which we inserted a hyperlink to WebWatcher (line 6). The user follows this hyperlink and gets to a page which allows her to identify the type of information she seeks. In this scenario the user is looking for a publication and selects the category \paper". She is presented a form to elaborate the information request ( gure 2). The user can ll in arbitrarily many keywords or leave elds blank. After that the user is sent back to the page from which she entered the WebWatcher system ( gure 3). But now WebWatcher is \looking over her shoulder" and modi es the page in three ways. (1) WebWatcher inserts a menubar on top of the original page. This menubar allows the user to invoke additional functions of WebWatcher or to terminate the search. (2) WebWatcher suggests additional hyperlinks above the menubar ( gure 3, line 2). (3) WebWatcher highlights hyperlinks in the actual page which seem interesting according to the information seeking goal. The system highlights hyperlinks by putting \eyes" around them ( gure 3, line 13). The size of the eye icon is a measure of WebWatcher's con dence in the advice. In our example the user follows WebWatcher's advice and takes the \ILPNET" hyperlink. She arrives at the page shown in gure 4. Until the user quits the search, WebWatcher will insert the menubar into the original page and give advice. While WebWatcher suggests which hyperlinks the user should take, the user remains rmly in control and may ignore the system's advice at any time. We think this is important because WebWatcher may provide imperfect advice, and because WebWatcher might not perfectly understand the user's information seeking
Tom Dayne Thorsten Projects Katharina MLResour ILPNet WWatcher LearnLab
Figure 6: Simple example of WWW structure.
Figure 5: Related pages goal. In our scenario the user is particularly interested in the \ILPNet" page. So she clicks on the button \Mark this page as interesting" in the menubar. WebWatcher stores this information and returns a list of 10 pages which WebWatcher estimates to be closely related ( gure 5). The user can leave WebWatcher at any time by clicking on \Goodbye. Information found." or \Goodbye. I give up." in the menubar.
This paper describes the rst implementation of WebWatcher, a Learning Apprentice for the World Wide Web. We also explore the possibility of extracting information from the structure of hypertext. We introduce an algorithm which identi es pages that are related to a given page using only hypertext structure. We motivate the algorithm by using the Minimum Description Length principle.
2 WebWatcher
Figure 1: HTML-page with hyperlink to WebWatcher
Figure 3: Original page (Abb. 1) with WebWatcher
Figure 2: Paper search form
Figure 4: Next page (us)
WebWatcher: Machine Learning and Hypertext
Thorsten Joachims, Tom Mitchell, Dayne Freitag, and Robert Armstrong
School of Computer Science Carnegie Mellon University May 29, 1995
Abstract
1 Introduction
The World Wide Web is growing quickly and addresses more and more users. Although a lot of information is available in the World Wide Web (WWW), it is di cult to nd particular pieces of information efciently. Many have noted the need for software that helps the user search for information. This paper describes the design of WebWatcher Armstrong et al., 1995], an agent which assists users in locating information on the WWW or searches autonomously on their behalf. In interactive mode WebWatcher acts as a Learning Apprentice Mitchell et al., 1985] Mitchell et. al., 1994]. It follows the user on his or her way through the World Wide Web and suggests hyperlinks whenever it is con dent enough. WebWatcher learns by observing the user's reaction to this advice as well as by the eventual success or failure of the user's actions. The rst implementation supports only this interactive mode. First we describe the design and the initial implementation of WebWatcher. We then introduce an algorithm which is used in WebWatcher to suggest pages related to the current page. This section describes the design of WebWatcher and how it assists users in their search for information. The system can be installed easily on any HTML-page by inserting a hyperlink to the WebWatcher server. This allows having multiple instances of WebWatcher which are experts for certain parts of the WWW. A user enters WebWatcher by clicking on a hyperlink to the WebWatcher server and can specify his or her interests by giving keywords. After that WebWatcher takes the user back to the page from which he or she entered the system. From now on WebWatcher follows the user's actions and suggests hyperlinks using Email: < rst name>.<last name>@