001461576 000__ 03865cam\a22005777a\4500
001461576 001__ 1461576
001461576 003__ OCoLC
001461576 005__ 20230503003400.0
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001461576 008__ 230318s2023\\\\si\\\\\\o\\\\\000\0\eng\d
001461576 019__ $$a1373010695
001461576 020__ $$a9789811938887$$q(electronic bk.)
001461576 020__ $$a9811938881$$q(electronic bk.)
001461576 020__ $$z9811938873
001461576 020__ $$z9789811938870
001461576 0247_ $$a10.1007/978-981-19-3888-7$$2doi
001461576 035__ $$aSP(OCoLC)1373349034
001461576 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF
001461576 049__ $$aISEA
001461576 050_4 $$aQA76.9.A43
001461576 08204 $$a005.13$$223/eng/20230323
001461576 24500 $$aMetaheuristics for machine learning :$$bnew advances and tools /$$cMansour Eddaly, Bassem Jarboui, Patrick Siarry, editors.
001461576 260__ $$aSingapore :$$bSpringer,$$c2023.
001461576 300__ $$a1 online resource (231 p.).
001461576 4901_ $$aComputational Intelligence Methods and Applications
001461576 5050_ $$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.
001461576 506__ $$aAccess limited to authorized users.
001461576 520__ $$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.
001461576 650_0 $$aMetaheuristics.
001461576 650_0 $$aMachine learning.
001461576 655_0 $$aElectronic books.
001461576 7001_ $$aEddaly, Mansour.
001461576 7001_ $$aJarboui, Bassem.
001461576 7001_ $$aSiarry, Patrick.
001461576 77608 $$iPrint version:$$aEddaly, Mansour$$tMetaheuristics for Machine Learning$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789811938870
001461576 830_0 $$aComputational intelligence methods and applications.
001461576 852__ $$bebk
001461576 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-3888-7$$zOnline Access$$91397441.1
001461576 909CO $$ooai:library.usi.edu:1461576$$pGLOBAL_SET
001461576 980__ $$aBIB
001461576 980__ $$aEBOOK
001461576 982__ $$aEbook
001461576 983__ $$aOnline
001461576 994__ $$a92$$bISE