Linked e-resources
Details
Table of Contents
Intro; Preface; Overview; Audience; Organization; Contents; Acronyms; 1 Introduction; 1.1 Introduction; 1.2 Notation and Terminology; 1.3 Centrality and Diversity; 1.3.1 Representation; 1.3.2 Clustering and Classification; 1.3.3 Ranking; 1.3.4 Regression; 1.3.5 Social Networks and Recommendation Systems; 1.4 Summary; Bibliography; 2 Searching; 2.1 Introduction; 2.1.1 Exact Match; 2.1.2 Inexact Match; 2.1.3 Representation; 2.2 Proximity; 2.2.1 Distance Function; 2.2.2 Clustering; 2.2.3 Classification; 2.2.4 Information Retrieval; 2.2.5 Problem Solving in Artificial Intelligence (AI)
2.3 SummaryBibliography; 3 Representation; 3.1 Introduction; 3.2 Problem Solving in AI; 3.3 Vector Space Representation; 3.3.1 What is a Document?; 3.4 Representing Text Documents; 3.4.1 Analysis of Text Documents; 3.5 Representing a Cluster; 3.5.1 Centroid; 3.5.2 Hierarchical Clustering; 3.6 Representing Classes and Classifiers; 3.6.1 Neighborhood Based Classifier (NNC); 3.6.2 Bayes Classifier; 3.6.3 Neural Net Classifiers; 3.6.4 Decision Tree Classifiers (DTC); 3.7 Summary; Bibliography; 4 Clustering and Classification; 4.1 Introduction; 4.2 Clustering
4.2.1 Clustering-Based Matrix Factorization4.2.2 Feature Selection; 4.2.3 Principal Component Analysis (PCA); 4.2.4 Singular Value Decomposition (SVD); 4.2.5 Diversified Clustering; 4.3 Classification; 4.3.1 Perceptron; 4.3.2 Support Vector Machine (SVM); 4.3.3 Summary; Bibliography; 5 Ranking; 5.1 Introduction; 5.2 Ranking Based on Similarity; 5.3 Ranking Based on Density; 5.4 Centrality and Diversity in Ranking; 5.4.1 Diversification Based on a Taxonomy; 5.5 Ranking Sentences for Extractive Summarization; 5.6 Diversity in Recommendations; 5.7 Summary; Bibliography
6 Centrality and Diversity in Social and Information Networks6.1 Introduction; 6.2 Representation; 6.3 Matrix Representation of Networks; 6.4 Link Prediction; 6.4.1 LP Algorithms; 6.5 Social and Information Networks; 6.6 Important Properties of Social Networks; 6.7 Centrality in Social Networks; 6.7.1 Degree Centrality; 6.7.2 Closeness Centrality; 6.7.3 Betweenness Centrality; 6.7.4 Eigenvector Centrality; 6.8 Community Detection; 6.9 Network Embedding; 6.9.1 Node Embeddings Based on Centrality; 6.9.2 Linear Embedding of Nodes Using PCA; 6.9.3 Random Walk-Based Models for Node Embedding
6.10 Combining Structure and Content6.11 Summary; Bibliography; 7 Conclusion; Glossary; Index
2.3 SummaryBibliography; 3 Representation; 3.1 Introduction; 3.2 Problem Solving in AI; 3.3 Vector Space Representation; 3.3.1 What is a Document?; 3.4 Representing Text Documents; 3.4.1 Analysis of Text Documents; 3.5 Representing a Cluster; 3.5.1 Centroid; 3.5.2 Hierarchical Clustering; 3.6 Representing Classes and Classifiers; 3.6.1 Neighborhood Based Classifier (NNC); 3.6.2 Bayes Classifier; 3.6.3 Neural Net Classifiers; 3.6.4 Decision Tree Classifiers (DTC); 3.7 Summary; Bibliography; 4 Clustering and Classification; 4.1 Introduction; 4.2 Clustering
4.2.1 Clustering-Based Matrix Factorization4.2.2 Feature Selection; 4.2.3 Principal Component Analysis (PCA); 4.2.4 Singular Value Decomposition (SVD); 4.2.5 Diversified Clustering; 4.3 Classification; 4.3.1 Perceptron; 4.3.2 Support Vector Machine (SVM); 4.3.3 Summary; Bibliography; 5 Ranking; 5.1 Introduction; 5.2 Ranking Based on Similarity; 5.3 Ranking Based on Density; 5.4 Centrality and Diversity in Ranking; 5.4.1 Diversification Based on a Taxonomy; 5.5 Ranking Sentences for Extractive Summarization; 5.6 Diversity in Recommendations; 5.7 Summary; Bibliography
6 Centrality and Diversity in Social and Information Networks6.1 Introduction; 6.2 Representation; 6.3 Matrix Representation of Networks; 6.4 Link Prediction; 6.4.1 LP Algorithms; 6.5 Social and Information Networks; 6.6 Important Properties of Social Networks; 6.7 Centrality in Social Networks; 6.7.1 Degree Centrality; 6.7.2 Closeness Centrality; 6.7.3 Betweenness Centrality; 6.7.4 Eigenvector Centrality; 6.8 Community Detection; 6.9 Network Embedding; 6.9.1 Node Embeddings Based on Centrality; 6.9.2 Linear Embedding of Nodes Using PCA; 6.9.3 Random Walk-Based Models for Node Embedding
6.10 Combining Structure and Content6.11 Summary; Bibliography; 7 Conclusion; Glossary; Index