Machine Learning: ECML 2006 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings / edited by Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou.

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Fürnkranz, Johannes (Editor), Scheffer, Tobias (Editor), Spiliopoulou, Myra (Editor)
Format:	eBook
Language:	English
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edition:	1st ed. 2006.
Series:	Lecture Notes in Artificial Intelligence ; 4212 Springer eBook Collection.
Subjects:	Artificial intelligence. Algorithms. Mathematical logic. Database management. Electronic resources (E-books)
Online Access:	Click to view e-book
Holy Cross Note:	Loaded electronically. Electronic access restricted to members of the Holy Cross Community.

Table of Contents:

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.

Machine Learning: ECML 2006 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings / edited by Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou.

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