M.N. Murty's Books

Machine Learning in Social Networks

Embedding Nodes, Edges, Communities, and Graphs

By: Manasvi Aggarwal,M.N. Murty

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Machine Learning in Social Networks

Embedding Nodes, Edges, Communities, and Graphs

By: Manasvi Aggarwal,M.N. Murty

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties.

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Page Count

121

Publisher

Springer Nature

Published Date

ISBN 10

9813340223

ISBN 13

9789813340220

Centrality and Diversity in Search

Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

By: M.N. Murty,Anirban Biswas

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Centrality and Diversity in Search

Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

By: M.N. Murty,Anirban Biswas

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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Page Count

100

Publisher

Springer

Published Date

ISBN 10

3030247139

ISBN 13

9783030247133

Environmental Valuation in South Asia

By: A. K. Enamul Haque,M. N. Murty,Priya Shyamsundar

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Environmental Valuation in South Asia

By: A. K. Enamul Haque,M. N. Murty,Priya Shyamsundar

This book is about understanding the value of environmental services in South Asia. It provides an overview of different environmental problems in South Asia and examines how economic valuation techniques can be used to assess these problems. It brings together multiple case studies on valuation undertaken by economists and environmental scientists from Bangladesh, India, Pakistan, Nepal and Sri Lanka under the aegis of the South Asian Network for Development and Environmental Economics (SANDEE). The book addresses the challenges of valuing environmental changes that are unique to developing countries. Each chapter starts with a description of an environmental problem and the valuation strategy used, followed by a discussion of estimation methods and results. It is designed to serve as a reference book for students, teachers, researchers, non-government organizations and practitioners of environmental valuation. Those interested in development and environmental economics, and natural resource management policies, will also find it useful.

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Page Count

499

Publisher

Cambridge University Press

Published Date

ISBN 10

1139504789

ISBN 13

9781139504782

Representation in Machine Learning

By: M. N. Murty,M. Avinash

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Representation in Machine Learning

By: M. N. Murty,M. Avinash

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.

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Page Count

101

Publisher

Springer Nature

Published Date

ISBN 10

9811979081

ISBN 13

9789811979088

Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

By: M.N. Murty,Rashmi Raghava

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Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

By: M.N. Murty,Rashmi Raghava

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

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Page Count

103

Publisher

Springer

Published Date

ISBN 10

3319410636

ISBN 13

9783319410630

Representation in Machine Learning

By: M. N. Murty,M. Avinash

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Representation in Machine Learning

By: M. N. Murty,M. Avinash

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Solve jigsaw puzzle

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Page Count

101

Publisher

Springer Nature

Published Date

ISBN 10

9811979081

ISBN 13

9789811979088

Centrality and Diversity in Search

Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

By: M.N. Murty,Anirban Biswas

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Centrality and Diversity in Search

Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition

By: M.N. Murty,Anirban Biswas

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N/A

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Solve jigsaw puzzle

Age

Page Count

100

Publisher

Springer

Published Date

ISBN 10

3030247139

ISBN 13

9783030247133

Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

By: M.N. Murty,Rashmi Raghava

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Support Vector Machines and Perceptrons

Learning, Optimization, Classification, and Application to Social Networks

By: M.N. Murty,Rashmi Raghava

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Solve jigsaw puzzle

Age

Page Count

103

Publisher

Springer

Published Date

ISBN 10

3319410636

ISBN 13

9783319410630

Machine Learning in Social Networks

Embedding Nodes, Edges, Communities, and Graphs

By: Manasvi Aggarwal,M.N. Murty

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Machine Learning in Social Networks

Embedding Nodes, Edges, Communities, and Graphs

By: Manasvi Aggarwal,M.N. Murty

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

121

Publisher

Springer Nature

Published Date

ISBN 10

9813340223

ISBN 13

9789813340220

Book's by M.N. Murty

Machine Learning in Social Networks

Machine Learning in Social Networks

Centrality and Diversity in Search

Centrality and Diversity in Search

Environmental Valuation in South Asia

Environmental Valuation in South Asia

Representation in Machine Learning

Representation in Machine Learning

Support Vector Machines and Perceptrons

Support Vector Machines and Perceptrons

Representation in Machine Learning

Representation in Machine Learning

Centrality and Diversity in Search

Centrality and Diversity in Search

Support Vector Machines and Perceptrons

Support Vector Machines and Perceptrons

Machine Learning in Social Networks

Machine Learning in Social Networks

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