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001			86478
003			ES-MaUEC
005			20240111050127.0
007			cr nn 008mamaa
008			160504s2016 gw a s 001 0 eng d
020			_a9783662483954
024	7		_a10.1007/978-3-662-48395-4 _2doi
040			_aES-MaUEC
050		4	_aQ325.5 _b.T67 2016 EB
082	0	4	_a004.0151
245	0	0	_aTopics in Grammatical Inference _cedited by Jeffrey Heinz, José M. Sempere
264		1	_aBerlin, Heidelberg _bSpringer Berlin Heidelberg _c2016
300			_a1 recurso en línea (XVII, 247 p.) _b56 ilustraciones, 7 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences.
520	3		_aThis book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aAprendizaje automático _0comprobar BNE20060904520 _2embne _9166090
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
700	1		_aHeinz, Jeffrey _eeditor literario _9100209 _0Local
700	1		_aSempere, José M. _eeditor literario _930146 _0comprobar BNE20000241197
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-662-48395-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783662483954
907			_a.b12957562 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b27-09-17 _cm _dz _ei _feng _ggw _h0
945			_aQ325.5 .T67 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11599339 _z06-04-17
999			_c86478 _d86478 _x1