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008			150903s2007 gw | o |||| 0|eng d
020			_a9783540749585 _99783540749585
024	7		_a10.1007/9783540749585 _2doi
035			_avtls000350996
039		9	_a201509030431 _bVLOAD _c201405060243 _dVLOAD _y201402171110 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ334-342
100	1		_aKok, Joost N. _eeditor. _9328979
245	1	0	_aMachine Learning: ECML 2007 : _b18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings / _cedited by Joost N. Kok, Jacek Koronacki, Raomon Lopez de Mantaras, Stan Matwin, Dunja Mladeni?, Andrzej Skowron.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2007.
300			_axxiv, 809 páginas _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 ; _v4701
500			_aSpringer eBooks
505	0		_aInvited Talks -- Learning, Information Extraction and the Web -- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation -- Mining Queries -- Adventures in Personalized Information Access -- Long Papers -- Statistical Debugging Using Latent Topic Models -- Learning Balls of Strings with Correction Queries -- Neighborhood-Based Local Sensitivity -- Approximating Gaussian Processes with -Matrices -- Learning Metrics Between Tree Structured Data: Application to Image Recognition -- Shrinkage Estimator for Bayesian Network Parameters -- Level Learning Set: A Novel Classifier Based on Active Contour Models -- Learning Partially Observable Markov Models from First Passage Times -- Context Sensitive Paraphrasing with a Global Unsupervised Classifier -- Dual Strategy Active Learning -- Decision Tree Instability and Active Learning -- Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering -- The Cost of Learning Directed Cuts -- Spectral Clustering and Embedding with Hidden Markov Models -- Probabilistic Explanation Based Learning -- Graph-Based Domain Mapping for Transfer Learning in General Games -- Learning to Classify Documents with Only a Small Positive Training Set -- Structure Learning of Probabilistic Relational Models from Incomplete Relational Data -- Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA -- Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures -- Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs -- Source Separation with Gaussian Process Models -- Discriminative Sequence Labeling by Z-Score Optimization -- Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches -- Bayesian Inference for Sparse Generalized Linear Models -- Classifier Loss Under Metric Uncertainty -- Additive Groves of Regression Trees -- Efficient Computation of Recursive Principal Component Analysis for Structured Input -- Hinge Rank Loss and the Area Under the ROC Curve -- Clustering Trees with Instance Level Constraints -- On Pairwise Naive Bayes Classifiers -- Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models -- Safe Q-Learning on Complete History Spaces -- Random k-Labelsets: An Ensemble Method for Multilabel Classification -- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble -- Avoiding Boosting Overfitting by Removing Confusing Samples -- Planning and Learning in Environments with Delayed Feedback -- Analyzing Co-training Style Algorithms -- Policy Gradient Critics -- An Improved Model Selection Heuristic for AUC -- Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators -- Short Papers -- Stepwise Induction of Multi-target Model Trees -- Comparing Rule Measures for Predictive Association Rules -- User Oriented Hierarchical Information Organization and Retrieval -- Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition -- Weighted Kernel Regression for Predicting Changing Dependencies -- Counter-Example Generation-Based One-Class Classification -- Test-Cost Sensitive Classification Based on Conditioned Loss Functions -- Probabilistic Models for Action-Based Chinese Dependency Parsing -- Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search -- A Simple Lexicographic Ranker and Probability Estimator -- On Minimizing the Position Error in Label Ranking -- On Phase Transitions in Learning Sparse Networks -- Semi-supervised Collaborative Text Classification -- Learning from Relevant Tasks Only -- An Unsupervised Learning Algorithm for Rank Aggregation -- Ensembles of Multi-Objective Decision Trees -- Kernel-Based Grouping of Histogram Data -- Active Class Selection -- Sequence Labeling with Reinforcement Learning and Ranking Algorithms -- Efficient Pairwise Classification -- Scale-Space Based Weak Regressors for Boosting -- K-Means with Large and Noisy Constraint Sets -- Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction -- Principal Component Analysis for Large Scale Problems with Lots of Missing Values -- Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling -- Class Noise Mitigation Through Instance Weighting -- Optimizing Feature Sets for Structured Data -- Roulette Sampling for Cost-Sensitive Learning -- Modeling Highway Traffic Volumes -- Undercomplete Blind Subspace Deconvolution Via Linear Prediction -- Learning an Outlier-Robust Kalman Filter -- Imitation Learning Using Graphical Models -- Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks -- Semi-definite Manifold Alignment -- General Solution for Supervised Graph Embedding -- Multi-objective Genetic Programming for Multiple Instance Learning -- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning.
520			_aThis book constitutes the refereed proceedings of the 18th European Conference on Machine Learning, ECML 2007, held in Warsaw, Poland, September 17-21, 2007, jointly with PKDD 2007. The 41 revised full papers and 37 revised short papers presented together with abstracts of 4 invited talks were carefully reviewed and selected from 592 abstracts submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKoronacki, Jacek. _eeditor. _9334483
700	1		_aMantaras, Raomon Lopez de. _eeditor. _9334484
700	1		_aMatwin, Stan. _eeditor. _9332271
700	1		_aMladeni?, Dunja. _eeditor. _9331985
700	1		_aSkowron, Andrzej. _eeditor. _9325942
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540749578
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-74958-5 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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