000			03435cam a2200409 i 4500
001			on1012838766
003			OCoLC
005			20250612155449.0
006			m o d
007			cr |||||||||||
008			170926s2017 ne o 000 0 eng d
040			_aNLE _beng _erda _epn _cNLE _dOCLCO _dOPELS _dYDX _dGZM _dOCLCF _dMERER _dUPM _dSNK _dOCLCQ _dD6H _dU3W _dOCLCQ _dWYU _dLVT _dLQU _dUKMGB _dS2H _dOCLCO _dOCLCQ _dSFB _dORMDA _dOCLCQ _dOCLCO _dOCLCL _dOCLCQ _dSXB
015			_aGBB7I8698 _2bnb
016	7		_a018544502 _2Uk
019			_a1014063614 _a1018202182 _a1105175446 _a1105562578
020			_a9780081006702 _q(ePub ebook)
020			_a0081006705 _q(ePub ebook)
020			_z9780081006597 _q(pbk.)
020			_z0081006594
035			_a(OCoLC)1012838766 _z(OCoLC)1014063614 _z(OCoLC)1018202182 _z(OCoLC)1105175446 _z(OCoLC)1105562578
050		4	_aQ325.5
082	0	4	_a006.3/1 _223
100	1		_aGori, Marco, _eauthor. _934017
245	1	0	_aMachine learning : _ba constraint-based approach / _cMarco Gori.
264		1	_aAmsterdam : _bMorgan Kaufmann, _c2017.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aThe Big Picture Learning Principles Linear-Threshold Machines Kernel Machines Deep Architectures Learning and Reasoning with Constraints Epilogue Answers to selected exercises Appendices: Constrained optimization in Finite Dimensions Regularization operators Calculus of variations Index to Notations.
520	8		_aAnnotation _bMachine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines.The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book.This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified mannerProvides in-depth coverage of unsupervised and semi-supervised learningIncludes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learningContains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex.
650		0	_aMachine learning.
650		0	_aAlgorithms.
758			_ihas work: _aMachine learning (Text) _1https://id.oclc.org/worldcat/entity/E39PCGMqwJkP4tmVh3B9KCXV3P _4https://id.oclc.org/worldcat/ontology/hasWork
776	0	8	_iPrint version: _z9780081006597
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780081006597
999			_c216420 _d216420