| Description |
1 online resource (100 pages) |
| Series |
Springerbriefs in intelligent systems, 2196-5498 |
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SpringerBriefs in intelligent systems
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| 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) |
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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 |
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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 |
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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 |
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6.10 Combining Structure and Content6.11 Summary; Bibliography; 7 Conclusion; Glossary; Index |
| Summary |
The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification. The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Artificial intelligence.
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Machine learning.
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Social networks.
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Pattern recognition systems.
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Artificial Intelligence
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Social Support
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Pattern Recognition, Automated
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Machine Learning
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artificial intelligence.
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Artificial intelligence
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Machine learning
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Pattern recognition systems
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Social networks
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| Form |
Electronic book
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| Author |
Biswas, Anirban, author
|
| ISBN |
9783030247133 |
|
3030247139 |
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9783030247126 |
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3030247120 |
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9783030247140 |
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3030247147 |
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