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Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization 1st Edition by Dionisios Sotiropoulos, George Tsihrintzis ISBN 3319836757 9783319836751

Name: Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization 1st Edition by Dionisios Sotiropoulos, George Tsihrintzis ISBN 3319836757 9783319836751
SKU: EB-5675822
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Machine Learning Paradigms: Artificial Immune Systems and their Applications in Software Personalization 1st Edition Dionisios N. Sotiropoulos Digital Instant Download

Author(s): Dionisios N. Sotiropoulos, George A. Tsihrintzis (auth.)

ISBN(s): 9783319471921, 9783319471945, 3319471929, 3319471945

Edition: 1

File Details: PDF, 6.56 MB

Year: 2017

Language: English

SKU: EB-5675822 Category: Computers Tags: Dionisios N. Sotiropoulos, George A. Tsihrintzis (auth.)

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Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization 1st Edition by Dionisios Sotiropoulos, George Tsihrintzis – Ebook PDF Instant Download/Delivery: 3319836757 ,9783319836751
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Product details:
ISBN 10: 3319836757
ISBN 13: 9783319836751
Author: Dionisios Sotiropoulos, George 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.

Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization 1st Edition Table of contents:

- Introduction to Artificial Immune Systems (AIS): A New Paradigm
  - Overview of biologically inspired computing.
  - Theoretical foundations of AIS.
  - Relevance to computational intelligence.
- Foundations of the Adaptive Immune System
  - Biological principles.
  - Analogies to software personalization.
  - Immune mechanisms as computational models.
- Machine Learning with AIS: Techniques and Applications
  - Clustering and classification algorithms.
  - One-class classification challenges.
  - Solutions for class imbalance problems.
- Development of Recommender Systems Using AIS
  - Personalized software applications.
  - Leveraging AIS for adaptive learning.
  - Case studies in recommender systems.
- Pattern Recognition Through AIS Algorithms
  - Application to binary classification.
  - Self/non-self discrimination.
  - Analysis of minority class detection.
- Experimental Results and Real-World Applications
  - Insights from diverse data sets.
  - Performance evaluation metrics.
  - Adaptability and scalability.
- Future Trends in AIS-Based Machine Learning
  - Emerging applications in artificial intelligence.
  - Integration with other computational paradigms.
  - Prospective challenges in software personalization.