机器学习

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Semantic Web and Knowledge GraphsBook

浏览 68 扫码 分享 2022-07-22 22:44:23
  • Semantic Web Mining 语义网挖掘
  • introduction
  • Foundation technologies
  • The Web Ontology Language 网络本体语言(OWL)
  • Knowledge Graphs

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  • 8715
  • LeetCode
  • Linux
  • 机器学习
    • 数据清洗
      • Lec 1-6
      • Lec 7-14
      • 12. Record linkage
      • 13-14. Pre-proccessing & Blocking
      • Lec 15 - 16 Record pair comparison
      • Lec 17-18 Record pair classification
      • 19. Record linkage evaluation
      • 20. Clerical review
      • 21. Data Fusion
      • 22. Advanced record linkage techniques
      • 23. Privacy aspects in data wrangling and privacy-preserving record linkage
      • 24. Ontology matching 本体匹配
      • 25. Wrangling dynamic and spatial data
    • 文档分析｜自然语言处理
      • IR
        • Information retrieval (IR) L1
        • Information retrieval (IR) L2
        • Information retrieval (IR) L3
        • Web search
      • Mechine learning
        • L1 Linear Regression
        • L2 Representation in NLP
        • L3 Deep Neural Networks
        • L4 Deep Neural Networks in Practice
        • L5 Deep Neural Networks for Structured Data
        • L6 DNN – Attention
        • L7 DNN-Transformers
        • L8 Pre-training and Neural Language Models
        • L9 Clustering
      • NLP
        • L1 Semantics
        • L2 Constituency Parsing 成分句法分析
        • L3 Dependency Parsing 依赖解析
        • L4 Language Models
    • 数据挖掘
      • 问题汇总
        • quiz
      • 分类和预测（Classification & Prediction）
        • Two steps: Construct and Evaluate
        • Evaluation
        • Decision Trees 决策树
          • Basic, greedy, decision tree algorithm
          • Attribute Selection Methods
            • Information Gain
            • Information Gain for continuous-valued attributes
            • Gain Ratio
            • Gini index
            • Other Attribute Selection Methods
          • Overfitting and tree pruning
          • Enhancements to the Basic Algorithm
          • Extracting Rules from Decision Trees
        • Rule learning 规则学习
          • Rule-Based Classification
          • Rule Induction
        • Bayes Classifiers 贝叶斯分类器
          • What is Bayesian classifier?
            • Basic Probabilities
            • Bayes' Theorem
            • Limitation
          • Naive Bayes 朴素贝叶斯
            • Laplacian Correction
            • Numerical attributes
          • Bayesian Belief Networks
            • Training a Belief Network
        • Evaluation of Classifiers
          • Model Evaluation and Selection
            • Evaluation metrics for classification
            • Estimating a classifier’s accuracy
            • ROC Curve
            • Comparing classifiers
            • Exercises
            • Other issues affecting the quality of a model
        • Lazy Learners
          • Case-Based Reasoning (CBR)
          • K-Nearest Neighbourhood (k-NN)
        • SVM(Support vector machine)支持向量机
          • Linearly Separable Case 线性可分
          • Linearly Inseparable Case 线性不可分情况
        • Neural Nets 神经网络
          • Multi-Layer Feed-Forward Neural Network
          • Prediction with Neural Network
          • Training a Neural Network
          • Performance Evaluation
          • Other Neural Net Architectures
        • Linear Regression 线性回归
          • Simple Linear Regression
          • Multiple Linear Regression
        • Variants of Classification
        • Other "Soft" Classification Algorithms
      • Cluster Analysis 聚类分析
        • Clustering: Basic Concepts
          • Quality of Clustering
          • Considerations
          • Major Approaches
        • Partitioning Methods (K-means)
          • Strength and Weakness
          • K-Medoids (PAM)
          • Exercise: K-means clustering
        • Hierarchical Clustering (AGNES and DIANA)
          • Distance between Clusters
          • Dendrogram 系统树图
        • Density-Based Clustering
        • Grid-Based Approach
        • Evaluation of Clustering
          • Assessing Clustering Tendency
          • Determine the Number of Clusters, k
          • Measure Clustering Quality
      • Outlier Detection
        • What are Outliers?
        • Statistical Approaches
          • Parametric Methods: Univariate Outliers from a Normal Distribution
          • Parametric Methods: Multivariate Outliers
          • Parametric Methods: Mixture of Parametric Distributions
        • Proximity-Based Approaches 基于邻近的方法
          • Distance-Based Outlier Detection: Nested loop method
            • Example: Distance-Based Outlier with Nested Loop
          • Density-based Outlier Detection
        • Clustering Based Approaches 基于聚类的方法
        • Classification-Based Approaches
        • Contextual and Collective Outliers
      • Specialist topics
        • Data Stream Mining 数据流挖掘
          • Stream OLAP
          • Synopsis Methods for Stream Data Processing 流数据处理的简要方法
          • Frequent Pattern Mining
          • Clustering
        • Ensemble Methods
          • Bagging
          • Boosting with AdaBoost
            • Example: AdaBoost ensemble method
        • Time Series Analysis 时间序列分析
          • Trend Analysis
          • Similarity Search 相似性搜索
      • Text and Web Mining
        • Basic Measures for Information Retrieval
        • Web mining
          • Mining Page Structure
          • Mining Web link structure
          • Mining Multimedia on the Web
          • Web Usage Mining
        • Word meaning by embedding
        • Text mining problems
          • Keyword based association analysis
          • Text Classification
          • Document Clustering
        • Text Data Analysis and Information Retrieval
          • Information Retrieval Techniques
          • Selecting index terms
          • building an index
          • Matching the query to the index
          • How to assign weights to term occurrences
          • Putting it together: Ranking in the vector space model
      • Semantic Web and Knowledge GraphsBook
        • Semantic Web Mining 语义网挖掘
          • OWL learning: Example
          • Refinement-based Classification
          • Summary
        • introduction
        • Foundation technologies
          • Universal Resource Identifiers
          • URI structure
          • RDF/RDFs
          • RDF for relations
          • How to encode Literals that are not things
          • Namespace abbreviations
          • A graph is a set of Triples
          • Typed Literals
          • Things can also be typed
          • SPARQL: Querying RDF
        • The Web Ontology Language 网络本体语言(OWL)
          • OWL preliminaries
          • OWL Classes
          • Relating Classes and Properties
          • OWL individuals
        • Knowledge Graphs
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