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008			210208s2021 gw | s |||| 0|eng d
020			_a9783030659271
024	7		_a10.1007/978-3-030-65927-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aBi, Y. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677858 _q(Ying)
245	1	0	_aGenetic Programming for Image Classification : _bAn Automated Approach to Feature Learning _cby Ying Bi, Bing Xue, Mengjie Zhang.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XXVIII, 258 páginas) _b92 ilustraciones, 59 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aAdaptation Learning and Optimization _x1867-4534 _v24
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_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.
520	3		_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.
988			_aSpringer_Robotics_2021
650		7	_aAprendizaje automático _2embne _9166090
650		7	_aComputación evolutiva _2embne _9667195
650		7	_aProgramación genética (Informática) _2embne _9469951
700	1		_aXue, Bing _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677859 _c(Senior lecturer in computer science)
700	1		_aZhang, Mengjie _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9677860
776	0	8	_iPrinted edition: _z9783030659264
776	0	8	_iPrinted edition: _z9783030659288
776	0	8	_iPrinted edition: _z9783030659295
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-65927-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eo _zSI