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008			150903s2006 gw | o |||| 0|eng d
020			_a9783540466505 _99783540466505
024	7		_a10.1007/11894841 _2doi
035			_avtls000349661
039		9	_a201509030739 _bVLOAD _c201404121241 _dVLOAD _c201404091018 _dVLOAD _y201402071208 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ334-342
100	1		_aBalcázar, José L. _eeditor. _9332456
245	1	0	_aAlgorithmic Learning Theory : _b17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006. Proceedings / _cedited by José L. Balcázar, Philip M. Long, Frank Stephan.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006.
300			_axiii, 393 páginas Also available online. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aLecture Notes in Computer Science, _x0302-9743 ; _v4264
500			_aSpringer eBooks
505	0		_aEditors’ Introduction -- Editors’ Introduction -- Invited Contributions -- Solving Semi-infinite Linear Programs Using Boosting-Like Methods -- e-Science and the Semantic Web: A Symbiotic Relationship -- Spectral Norm in Learning Theory: Some Selected Topics -- Data-Driven Discovery Using Probabilistic Hidden Variable Models -- Reinforcement Learning and Apprenticeship Learning for Robotic Control -- Regular Contributions -- Learning Unions of ?(1)-Dimensional Rectangles -- On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle -- Active Learning in the Non-realizable Case -- How Many Query Superpositions Are Needed to Learn? -- Teaching Memoryless Randomized Learners Without Feedback -- The Complexity of Learning SUBSEQ (A) -- Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data -- Learning and Extending Sublanguages -- Iterative Learning from Positive Data and Negative Counterexamples -- Towards a Better Understanding of Incremental Learning -- On Exact Learning from Random Walk -- Risk-Sensitive Online Learning -- Leading Strategies in Competitive On-Line Prediction -- Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring -- General Discounting Versus Average Reward -- The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection -- Is There an Elegant Universal Theory of Prediction? -- Learning Linearly Separable Languages -- Smooth Boosting Using an Information-Based Criterion -- Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice -- Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence -- Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning -- Unsupervised Slow Subspace-Learning from Stationary Processes -- Learning-Related Complexity of Linear Ranking Functions.
520			_aThis book constitutes the refereed proceedings of the 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006. The 24 revised full papers presented together with the abstracts of 5 invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, reinforcement learning, and statistical learning models.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aLong, Philip M. _eeditor. _9332457
700	1		_aStephan, Frank. _eeditor. _9332458
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540466499
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/11894841 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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