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020 _a9789811938887
_9978-981-19-3888-7
024 7 _a10.1007/978-981-19-3888-7
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
245 1 0 _aMetaheuristics for Machine Learning
_h[electronic resource] :
_bNew Advances and Tools /
_cedited by Mansour Eddaly, Bassem Jarboui, Patrick Siarry.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXV, 223 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _a1. 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.
520 _aUsing 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.
650 0 _aMachine learning.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 1 4 _aMachine Learning.
650 2 4 _aArtificial Intelligence.
650 2 4 _aTheory and Algorithms for Application Domains.
700 1 _aEddaly, Mansour.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aJarboui, Bassem.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSiarry, Patrick.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811938870
776 0 8 _iPrinted edition:
_z9789811938894
776 0 8 _iPrinted edition:
_z9789811938900
830 0 _aComputational Intelligence Methods and Applications,
_x2510-1773
856 4 0 _uhttps://doi.org/10.1007/978-981-19-3888-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178133
_d178133