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001433864 001__ 1433864
001433864 003__ OCoLC
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001433864 019__ $$a1237526169$$a1244118663
001433864 020__ $$a9783030659271$$q(electronic bk.)
001433864 020__ $$a3030659275$$q(electronic bk.)
001433864 020__ $$z3030659267
001433864 020__ $$z9783030659264
001433864 0247_ $$a10.1007/978-3-030-65927-1$$2doi
001433864 035__ $$aSP(OCoLC)1237407901
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001433864 049__ $$aISEA
001433864 050_4 $$aQA76.623
001433864 08204 $$a006.3/1$$223
001433864 1001_ $$aBi, Ying,$$eauthor.
001433864 24510 $$aGenetic programming for image classification :$$ban automated approach to feature learning /$$cYing Bi, Bing Xue, Mengjie Zhang.
001433864 264_1 $$aCham :$$bSpringer,$$c[2021]
001433864 300__ $$a1 online resource (279 pages)
001433864 336__ $$atext$$btxt$$2rdacontent
001433864 337__ $$acomputer$$bc$$2rdamedia
001433864 338__ $$aonline resource$$bcr$$2rdacarrier
001433864 347__ $$atext file
001433864 347__ $$bPDF
001433864 4901_ $$aAdaptation, learning, and optimization ;$$vvolume 24
001433864 504__ $$aIncludes bibliographical references and index.
001433864 5050_ $$aComputer Vision and Machine Learning -- Evolutionary Computation and Genetic Programming -- Multi-Layer Representation for Binary Image Classification -- Evolutionary Deep Learning Using GP with Convolution Operators -- GP with Image Descriptors for Learning Global and Local Features -- GP with Image-Related Operators for Feature Learning -- GP for Simultaneous Feature Learning and Ensemble Learning -- Random Forest-Assisted GP for Feature Learning -- Conclusions and Future Directions.
001433864 506__ $$aAccess limited to authorized users.
001433864 520__ $$aThis book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
001433864 588__ $$aDescription based on print version record.
001433864 650_0 $$aGenetic programming (Computer science)
001433864 650_0 $$aPattern recognition systems.
001433864 650_0 $$aComputer vision.
001433864 650_6 $$aProgrammation génétique (Informatique)
001433864 650_6 $$aReconnaissance des formes (Informatique)
001433864 650_6 $$aVision par ordinateur.
001433864 655_0 $$aElectronic books.
001433864 7001_ $$aXue, Bing$$c(Senior lecturer in computer science),$$eauthor.
001433864 7001_ $$aZhang, Mengjie,$$eauthor.
001433864 77608 $$iPrint version:$$aBi, Ying.$$tGenetic Programming for Image Classification.$$dCham : Springer International Publishing AG, ©2021$$z9783030659264
001433864 830_0 $$aAdaptation, learning and optimization ;$$vv. 24.
001433864 852__ $$bebk
001433864 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-65927-1$$zOnline Access$$91397441.1
001433864 909CO $$ooai:library.usi.edu:1433864$$pGLOBAL_SET
001433864 980__ $$aBIB
001433864 980__ $$aEBOOK
001433864 982__ $$aEbook
001433864 983__ $$aOnline
001433864 994__ $$a92$$bISE