Centrality and Diversity in Search : Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

Anirban Biswas

ISBN 10: 3030247120 / ISBN 13: 9783030247126

Published by Springer International Publishing, Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030247126

Bibliographic Details

• Title: Centrality and Diversity in Search : Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030247120
• ISBN 13: 9783030247126
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 7D millimeters 235 millimeters width by 155 millimeters height by 7 millimeters depth
• Weight: 178 grams

About this title

Synopsis

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.

 

About the Author

Dr. M.N. Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore, India. Anirban Biswas is a Teaching Assistant at the same institution.

Prof. Murty’s other publications include the Springer titles Support Vector Machines and Perceptrons, Compression Schemes for Mining Large Datasets, and Pattern Recognition: An Algorithmic Approach.