Centrality and Diversity in Search [electronic resource] : Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition /

By:

Murty, M.N [author.]

Contributor(s):

Material type: Text

TextSeries: SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive RoboticsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019Description: XI, 94 p. 17 illus., 5 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783030247133

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

006.31 23

LOC classification:

Q325.5-.7

Online resources:

Click here to access online

Contents:

Introduction -- Searching -- Representation -- Clustering and Classification -- Ranking -- Centrality and Diversity in Social and Information Networks -- Conclusion.

In: Springer Nature eBookSummary: 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. .

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Introduction -- Searching -- Representation -- Clustering and Classification -- Ranking -- Centrality and Diversity in Social and Information Networks -- Conclusion.

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. .

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