LEADER 07472nam 2200637 a 450 001 9910768437803321 005 20200520144314.0 010 $a3-540-46056-X 024 7 $a10.1007/11871842 035 $a(CKB)1000000000283917 035 $a(SSID)ssj0000318742 035 $a(PQKBManifestationID)11255710 035 $a(PQKBTitleCode)TC0000318742 035 $a(PQKBWorkID)10336032 035 $a(PQKB)11096760 035 $a(DE-He213)978-3-540-46056-5 035 $a(MiAaPQ)EBC3068466 035 $a(PPN)123138574 035 $a(EXLCZ)991000000000283917 100 $a20060809d2006 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine learning $eECML 2006 : 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006 : proceedings /$fJohannes Furnkranz, Tobias Scheffer, Myra Spiliopoulou (eds.) 205 $a1st ed. 2006. 210 $aBerlin ;$aNew York $cSpringer$dc2006 215 $a1 online resource (XXIII, 851 p.) 225 1 $aLecture notes in computer science. Lecture notes in artificial intelligence,$x0302-9743 ;$v4212 225 1 $aLNCS sublibrary. SL 7, Artificial intelligence 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-45375-X 320 $aIncludes bibliographical references and index. 327 $aInvited Talks -- On Temporal Evolution in Data Streams -- The Future of CiteSeer: CiteSeerx -- Learning to Have Fun -- Winning the DARPA Grand Challenge -- Challenges of Urban Sensing -- Long Papers -- Learning in One-Shot Strategic Form Games -- A Selective Sampling Strategy for Label Ranking -- Combinatorial Markov Random Fields -- Learning Stochastic Tree Edit Distance -- Pertinent Background Knowledge for Learning Protein Grammars -- Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies -- Sequence Discrimination Using Phase-Type Distributions -- Languages as Hyperplanes: Grammatical Inference with String Kernels -- Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning -- Fisher Kernels for Relational Data -- Evaluating Misclassifications in Imbalanced Data -- Improving Control-Knowledge Acquisition for Planning by Active Learning -- PAC-Learning of Markov Models with Hidden State -- A Discriminative Approach for the Retrieval of Images from Text Queries -- TildeCRF: Conditional Random Fields for Logical Sequences -- Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data -- Bayesian Learning of Markov Network Structure -- Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks -- Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions -- EM Algorithm for Symmetric Causal Independence Models -- Deconvolutive Clustering of Markov States -- Patching Approximate Solutions in Reinforcement Learning -- Fast Variational Inference for Gaussian Process Models Through KL-Correction -- Bandit Based Monte-Carlo Planning -- Bayesian Learning with Mixtures of Trees -- Transductive Gaussian Process Regression with Automatic Model Selection -- Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees -- Why Is Rule Learning Optimistic and How to Correct It -- Automatically Evolving Rule Induction Algorithms -- Bayesian Active Learning for Sensitivity Analysis -- Mixtures of Kikuchi Approximations -- Boosting in PN Spaces -- Prioritizing Point-Based POMDP Solvers -- Graph Based Semi-supervised Learning with Sharper Edges -- Margin-Based Active Learning for Structured Output Spaces -- Skill Acquisition Via Transfer Learning and Advice Taking -- Constant Rate Approximate Maximum Margin Algorithms -- Batch Classification with Applications in Computer Aided Diagnosis -- Improving the Ranking Performance of Decision Trees -- Multiple-Instance Learning Via Random Walk -- Localized Alternative Cluster Ensembles for Collaborative Structuring -- Distributional Features for Text Categorization -- Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data -- An Adaptive Kernel Method for Semi-supervised Clustering -- To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles -- Ensembles of Nearest Neighbor Forecasts -- Short Papers -- Learning Process Models with Missing Data -- Case-Based Label Ranking -- Cascade Evaluation of Clustering Algorithms -- Making Good Probability Estimates for Regression -- Fast Spectral Clustering of Data Using Sequential Matrix Compression -- An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects -- Efficient Inference in Large Conditional Random Fields -- A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses -- Cost-Sensitive Decision Tree Learning for Forensic Classification -- The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces -- Right of Inference: Nearest Rectangle Learning Revisited -- Reinforcement Learning for MDPs with Constraints -- Efficient Non-linear Control Through Neuroevolution -- Efficient Prediction-Based Validation for Document Clustering -- On Testing the Missing at Random Assumption -- B-Matching for Spectral Clustering -- Multi-class Ensemble-Based Active Learning -- Active Learning with Irrelevant Examples -- Classification with Support Hyperplanes -- (Agnostic) PAC Learning Concepts in Higher-Order Logic -- Evaluating Feature Selection for SVMs in High Dimensions -- Revisiting Fisher Kernels for Document Similarities -- Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery -- Robust Probabilistic Calibration -- Missing Data in Kernel PCA -- Exploiting Extremely Rare Features in Text Categorization -- Efficient Large Scale Linear Programming Support Vector Machines -- An Efficient Approximation to Lookahead in Relational Learners -- Improvement of Systems Management Policies Using Hybrid Reinforcement Learning -- Diversified SVM Ensembles for Large Data Sets -- Dynamic Integration with Random Forests -- Bagging Using Statistical Queries -- Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test -- Spline Embedding for Nonlinear Dimensionality Reduction -- Cost-Sensitive Learning of SVM for Ranking -- Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures. 410 0$aLecture notes in computer science ;$v4212. 410 0$aLecture notes in computer science.$pLecture notes in artificial intelligence. 410 0$aLecture notes in computer science.$pLecture notes in artificial intelligence ;$v4212. 410 0$aLNCS sublibrary.$nSL 7,$pArtificial intelligence. 517 3 $aECML 2006 517 3 $a17th European Conference on Machine Learning 517 3 $aSeventeenth European Conference on Machine Learning 517 3 $aEuropean Conference on Machine Learning 606 $aMachine learning$vCongresses 615 0$aMachine learning 676 $a006.3/1 701 $aFurnkranz$b Johannes$01756205 701 $aScheffer$b Tobias$01756206 701 $aSpiliopoulou$b Myra$01756207 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768437803321 996 $aMachine learning$94193333 997 $aUNINA