Machine Learning: ECML 2006 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Fürnkranz, Johannes (Editor ), Scheffer, Tobias (Editor ), Spiliopoulou, Myra (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edición:1st ed. 2006.
Colección:Lecture Notes in Artificial Intelligence, 4212
Temas:
Artificial intelligence.
Algorithms.
Machine theory.
Database management.
Artificial Intelligence.
Formal Languages and Automata Theory.
Database Management.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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