George A. Tsihrintzis's Books

Machine Learning Paradigms

Applications in Recommender Systems

By: Aristomenis S. Lampropoulos,George A. Tsihrintzis

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Machine Learning Paradigms

Applications in Recommender Systems

By: Aristomenis S. Lampropoulos,George A. Tsihrintzis

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.

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Page Count

135

Publisher

Springer

Published Date

ISBN 10

3319191357

ISBN 13

9783319191355

Machine Learning Paradigms

Artificial Immune Systems and their Applications in Software Personalization

By: Dionisios N. Sotiropoulos,George A. Tsihrintzis

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Machine Learning Paradigms

Artificial Immune Systems and their Applications in Software Personalization

By: Dionisios N. Sotiropoulos,George A. Tsihrintzis

The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor towards the creation of abstract and high level representations of biological components or functions that lay the foundations for an alternative machine learning paradigm. Therefore, focus is given on addressing the primary problems of Pattern Recognition by developing Artificial Immune System-based machine learning algorithms for the problems of Clustering, Classification and One-Class Classification. Pattern Classification, in particular, is studied within the context of the Class Imbalance Problem. The main source of inspiration stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems that is exceptionally evolved in order to continuously address an extremely unbalanced pattern classification problem, namely, the self / non-self discrimination process. The experimental results presented in this monograph involve a wide range of degenerate binary classification problems where the minority class of interest is to be recognized against the vast volume of the majority class of negative patterns. In this context, Artificial Immune Systems are utilized for the development of personalized software as the core mechanism behind the implementation of Recommender Systems. The book will be useful to researchers, practitioners and graduate students dealing with Pattern Recognition and Machine Learning and their applications in Personalized Software and Recommender Systems. It is intended for both the expert/researcher in these fields, as well as for the general reader in the field of Computational Intelligence and, more generally, Computer Science who wishes to learn more about the field of Intelligent Computing Systems and its applications. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

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Page Count

336

Publisher

Springer

Published Date

ISBN 10

3319471945

ISBN 13

9783319471945

Semantic Knowledge Modelling via Open Linked Ontologies

Ontologies in E-Governance

By: Stamatios Theocharis,George A. Tsihrintzis

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Semantic Knowledge Modelling via Open Linked Ontologies

Ontologies in E-Governance

By: Stamatios Theocharis,George A. Tsihrintzis

Evolving technological advances in Artificial Intelligence-empowered Software present significant potential to lead e-Government towards more collective efforts, exchange of experiences on best practices both at national and international levels and dissemination of secluded administrative knowledge. In this book, novel semantic web-based and linked open data-based approaches are developed for the modelling and management of the huge volume of administrative data and the procedures followed by public sector bodies and for the production and management of relevant administrative knowledge. The book consists of eight chapters, each of which includes relevant bibliographic references for deeper probing. Appendices complement this work with sections of configuration files of the applications developed and used. Professors, researchers, scientists, engineers and students in artificial intelligence, e-government and other computer science-related disciplines are expected to benefit greatly from it, along with non-specialist readers from other disciplines who are interested in getting versed in the recent developments in e-government.

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Page Count

381

Publisher

Springer Nature

Published Date

ISBN 10

3031205855

ISBN 13

9783031205859

Book's by George A. Tsihrintzis

Machine Learning Paradigms

Machine Learning Paradigms

Machine Learning Paradigms

Machine Learning Paradigms

Semantic Knowledge Modelling via Open Linked Ontologies

Semantic Knowledge Modelling via Open Linked Ontologies

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