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00000cam a2200000 i 4500 |
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on1373349034 |
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OCoLC |
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20241006213017.0 |
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m o d |
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230318s2023 si ob 000 0 eng d |
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|a EBLCP
|b eng
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|d OCLCQ
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| 019 |
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|a 1373010695
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|a 9789811938887
|q electronic book
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|a 9811938881
|q electronic book
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|z 9811938873
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|z 9789811938870
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7 |
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|a 10.1007/978-981-19-3888-7
|2 doi
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| 035 |
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|a (OCoLC)1373349034
|z (OCoLC)1373010695
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| 050 |
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4 |
|a QA76.9.A43
|b M48 2023
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| 072 |
|
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|a UYQM
|2 bicssc
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| 072 |
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|a COM004000
|2 bisacsh
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|a UYQM
|2 thema
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| 049 |
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|a HCDD
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| 245 |
0 |
0 |
|a Metaheuristics for machine learning :
|b new advances and tools /
|c Mansour Eddaly, Bassem Jarboui, Patrick Siarry, editors.
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| 264 |
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1 |
|a Singapore :
|b Springer,
|c [2023]
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| 300 |
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|a 1 online resource (231 p.).
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Computational Intelligence Methods and Applications
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| 504 |
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|a Includes bibliographical references.
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| 505 |
0 |
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|a 1. From metaheuristics to automatic programming -- 2. Biclustering Algorithms Based on Metaheuristics: A Review -- 3. A Metaheuristic Perspective on Learning Classifier Systems -- 4. An evolutionary clustering approach using metaheuristics and unsupervised machine learning algorithms for customer segmentation -- 5. Applications of Metaheuristics in Parameter Optimization in Manufacturing Processes and Machine Health Monitoring -- 6. Evolving Machine Learning-based classifiers by metaheuristic approaches for underwater sonar target detection and recognition -- 7. Solving the Quadratic Knapsack Problem using a GRASP algorithm based on a multi-swap local search -- 8. Algorithmic vs Processing Manipulations to Scale Genetic Programming to Big Data Mining -- 9. Dynamic assignment problem of parking slots.
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| 520 |
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|a Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
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| 588 |
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|a Description based on online resource; title from digital title page (viewed on August 02, 2023).
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| 650 |
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0 |
|a Metaheuristics.
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| 650 |
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0 |
|a Machine learning.
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| 650 |
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7 |
|a Machine learning
|2 fast
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| 650 |
|
7 |
|a Metaheuristics
|2 fast
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| 700 |
1 |
|
|a Eddaly, Mansour,
|e editor.
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| 700 |
1 |
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|a Jarboui, Bassem,
|e editor.
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| 700 |
1 |
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|a Siarry, Patrick,
|e editor.
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| 776 |
0 |
8 |
|i Print version:
|a Eddaly, Mansour
|t Metaheuristics for Machine Learning
|d Singapore : Springer Singapore Pte. Limited,c2023
|z 9789811938870
|
| 830 |
|
0 |
|a Computational intelligence methods and applications.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-981-19-3888-7
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
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|a 92
|b HCD
|