Machine Learning: ECML 2006 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings /
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2006.
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| Σειρά: | Lecture Notes in Computer Science,
4212 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
