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008			150903s2006 gw | o |||| 0|eng d
020			_a9783540352969 _99783540352969
024	7		_a10.1007/11776420 _2doi
035			_avtls000349058
039		9	_a201509030747 _bVLOAD _c201404121157 _dVLOAD _c201404090934 _dVLOAD _y201402071153 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA75.5-76.95
100	1		_aLugosi, Gábor. _eeditor. _9330064
245	1	0	_aLearning Theory : _b19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006. Proceedings / _cedited by Gábor Lugosi, Hans Ulrich Simon.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2006.
300			_axI, 654 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 ; _v4005
500			_aSpringer eBooks
505	0		_aInvited Presentations -- Random Multivariate Search Trees -- On Learning and Logic -- Predictions as Statements and Decisions -- Clustering, Un-, and Semisupervised Learning -- A Sober Look at Clustering Stability -- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption -- Stable Transductive Learning -- Uniform Convergence of Adaptive Graph-Based Regularization -- Statistical Learning Theory -- The Rademacher Complexity of Linear Transformation Classes -- Function Classes That Approximate the Bayes Risk -- Functional Classification with Margin Conditions -- Significance and Recovery of Block Structures in Binary Matrices with Noise -- Regularized Learning and Kernel Methods -- Maximum Entropy Distribution Estimation with Generalized Regularization -- Unifying Divergence Minimization and Statistical Inference Via Convex Duality -- Mercer’s Theorem, Feature Maps, and Smoothing -- Learning Bounds for Support Vector Machines with Learned Kernels -- Query Learning and Teaching -- On Optimal Learning Algorithms for Multiplicity Automata -- Exact Learning Composed Classes with a Small Number of Mistakes -- DNF Are Teachable in the Average Case -- Teaching Randomized Learners -- Inductive Inference -- Memory-Limited U-Shaped Learning -- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples -- Learning Rational Stochastic Languages -- Parent Assignment Is Hard for the MDL, AIC, and NML Costs -- Learning Algorithms and Limitations on Learning -- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention -- Discriminative Learning Can Succeed Where Generative Learning Fails -- Improved Lower Bounds for Learning Intersections of Halfspaces -- Efficient Learning Algorithms Yield Circuit Lower Bounds -- Online Aggregation -- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition -- Aggregation and Sparsity Via ?1 Penalized Least Squares -- A Randomized Online Learning Algorithm for Better Variance Control -- Online Prediction and Reinforcement Learning I -- Online Learning with Variable Stage Duration -- Online Learning Meets Optimization in the Dual -- Online Tracking of Linear Subspaces -- Online Multitask Learning -- Online Prediction and Reinforcement Learning II -- The Shortest Path Problem Under Partial Monitoring -- Tracking the Best Hyperplane with a Simple Budget Perceptron -- Logarithmic Regret Algorithms for Online Convex Optimization -- Online Variance Minimization -- Online Prediction and Reinforcement Learning III -- Online Learning with Constraints -- Continuous Experts and the Binning Algorithm -- Competing with Wild Prediction Rules -- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path -- Other Approaches -- Ranking with a P-Norm Push -- Subset Ranking Using Regression -- Active Sampling for Multiple Output Identification -- Improving Random Projections Using Marginal Information -- Open Problems -- Efficient Algorithms for General Active Learning -- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints.
520			_aThis book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aSimon, Hans Ulrich. _eeditor. _9329670
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540352945
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/11776420 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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