MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04428cam a22004695i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
21683935 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20231218114525.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr ||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
161026s2017 gw |||| o |||| 0|eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2019750139 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783319471945 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-3-319-47194-5 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(DE-He213)978-3-319-47194-5 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
FIT |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| Item number |
SOT-M 2017 790488 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sotiropoulos, Dionisios N. |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine Learning Paradigms : |
| Remainder of title |
Artificial Immune Systems and their Applications in Software Personalization / |
| Statement of responsibility, etc. |
by Dionisios N. Sotiropoulos, George A. Tsihrintzis. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2017. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Cham : |
| Name of producer, publisher, distributor, manufacturer |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2017. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (XVI, 327 pages 71 illustrations, 18 illustrations in color.) |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Intelligent Systems Reference Library, |
| International Standard Serial Number |
1868-4394 ; |
| Volume/sequential designation |
118 |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction -- Machine Learning -- The Class Imbalance Problem -- Addressing the Class Imbalance Problem -- Machine Learning Paradigms -- Immune System Fundamentals -- Artificial Immune Systems -- Experimental Evaluation of Artificial Immune System-based Learning Algorithms -- Conclusions and Future Work. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on publisher-supplied MARC data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational Intelligence. |
| Authority record control number |
https://scigraph.springernature.com/ontologies/product-market-codes/T11014 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial Intelligence. |
| Authority record control number |
https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tsihrintzis, George A. |
| Relator term |
author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| Title |
Machine learning paradigms. |
| International Standard Book Number |
9783319471921 |
| Record control number |
(DLC) 2016953915 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319471921 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319471938 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319836751 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Intelligent Systems Reference Library, |
| International Standard Serial Number |
1868-4394 ; |
| Volume number/sequential designation |
118 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |