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157250340
Intelligent Systems Reference Library Machine Learning Paradigms: Artificial Immune Systems and Their Applications in Software Personalization, Book
88% of respondents would recommend this to a friend
Č. položky:
157250340
€ 122
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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.
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What Stands Out
Comprehensive Coverage
Delves into various machine learning paradigms, particularly focusing on artificial immune systems, offering readers a well-rounded understanding essential for software personalization.
Practical Applications
Highlights real-world applications of artificial immune systems, providing insights into how they can be implemented for personalized software solutions, addressing user needs effectively.
Academic Rigor
Written by experts in the field, ensuring high-quality content that appeals to scholars and practitioners alike, establishing it as a reliable reference for advanced studies in machine learning.
Detaily produktu
| Book format | Hardcover |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | November, 2016 |
| Pages | 327 |
| Subgenre | Artificial Intelligence |
| Series title | Intelligent Systems Reference Library |
| Number in series | 118 |
| Edition | 2017 Edition |
| Publisher | Springer International Publishing |
| Original languages | English |
| Language | English |
| Edu focus | General |
| Educational level | Higher |
| Is collectible | N |
| Binding type | Case Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 0.81 x 9.21 in (15.6 x 2.1 x 23.4 cm) |
| Assembled product weight | 1.45 lb (660 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Machine Learning Researchers
Ideal for researchers focusing on innovative approaches to machine learning, specifically artificial immune systems and personalization methods.
-
Software Developers
Beneficial for developers interested in integrating advanced personalization strategies into applications using artificial immune systems.
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Students in AI
Useful for students studying artificial intelligence and machine learning paradigms, enhancing their understanding of specialized applications.
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General Readers
May not appeal to casual readers lacking technical background in artificial intelligence and machine learning concepts.
OPIS PRODUKTU
Intelligent Systems Reference Library Machine Learning Paradigms: Artificial Immune Systems and Their Applications in Software Personalization, Book 118, (Hardcover)
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Dionisios N Sotiropoulos All Books Editorial Review
Intelligent Systems Reference Library Machine Learning Paradigms: Artificial Immune Systems and Their Applications in Software Personalization is a comprehensive hardcover book that explores advanced concepts in artificial intelligence, particularly focusing on how artificial immune systems can be applied to software personalization. This non-fiction work, published in November 2016, spans 327 pages and is part of the esteemed Intelligent Systems Reference Library series. Readers will appreciate the in-depth analysis of machine learning paradigms, making it an essential resource for higher education and industry professionals alike. Its concise dimensions and case binding create an ideal format for both academic and casual readers interested in computing and internet technologies.
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Pros
- Comprehensive coverage of artificial intelligence concepts
- Well-structured and informative for higher education
- Useful in both academia and industry applications
- Covers software personalization strategies effectively
- Part of a reputable series in intelligent systems
Zápory
- May not appeal to casual readers outside of academia
Product Price History
Dôležité informácie
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€ 122
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Funkcie a výhody
- Explore biologically inspired computing paradigms.
- Delve into artificial immune systems for machine learning.
- Address primary problems in pattern recognition.
- Develop algorithms for clustering, classification, and more.
- Gain insights into personalized software and recommender systems.
- Perfect for researchers, practitioners, and students in computational intelligence.
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