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978-3-540-46056-5 |
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100301s2006 gw | s |||| 0|eng d |
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|a 9783540460565
|9 978-3-540-46056-5
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|a 10.1007/11871842
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|a Machine Learning: ECML 2006
|h [electronic resource] :
|b 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings /
|c edited by Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou.
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|a 1st ed. 2006.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2006.
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|a XXIII, 851 p.
|b online resource.
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|a text
|b txt
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|a computer
|b c
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|a text file
|b PDF
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 4212
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| 505 |
0 |
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|a Invited 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.
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| 650 |
|
0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Algorithms.
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| 650 |
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0 |
|a Machine theory.
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| 650 |
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0 |
|a Database management.
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| 650 |
1 |
4 |
|a Artificial Intelligence.
|
| 650 |
2 |
4 |
|a Algorithms.
|
| 650 |
2 |
4 |
|a Formal Languages and Automata Theory.
|
| 650 |
2 |
4 |
|a Database Management.
|
| 700 |
1 |
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|a Fürnkranz, Johannes.
|e editor.
|0 (orcid)0000-0002-1207-0159
|1 https://orcid.org/0000-0002-1207-0159
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Scheffer, Tobias.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Spiliopoulou, Myra.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540830719
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783540453758
|
| 830 |
|
0 |
|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 4212
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| 856 |
4 |
0 |
|u https://doi.uam.elogim.com/10.1007/11871842
|z Texto Completo
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
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