Myra Spiliopoulou, Johannes F?rnkranz, Tobias Scheffer




Machine Learning: ECML 2006




Springer Berlin Heidelberg

BOOK (manifestation)

  • Book: 978-3-540-45375-8 (Book)
  • Book: 978-3-540-46056-5 (eBook)

  • Related objects


  • Max Planck Institute For Coal Research
  • Otto-Von-Guericke University Magdeburg
  • Technical University Of Darmstadt


  • Conference: European Conference On Machine Learning


  • BookChapter: Patching Approximate Solutions in Reinforcement Learning
  • BookChapter: Evaluating Feature Selection for SVMs in High Dimensions
  • BookChapter: An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects
  • BookChapter: A Selective Sampling Strategy for Label Ranking
  • BookChapter: Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test
  • BookChapter: A Discriminative Approach for the Retrieval of Images from Text Queries
  • BookChapter: Sequence Discrimination Using Phase-Type Distributions
  • BookChapter: Multi-class Ensemble-Based Active Learning
  • BookChapter: Classification with Support Hyperplanes
  • BookChapter: Languages as Hyperplanes: Grammatical Inference with String Kernels
  • BookChapter: Improvement of Systems Management Policies Using Hybrid Reinforcement Learning
  • BookChapter: The Future of CiteSeer: CiteSeer x
  • BookChapter: Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies
  • BookChapter: Dynamic Integration with Random Forests
  • BookChapter: Prioritizing Point-Based POMDP Solvers
  • BookChapter: An Adaptive Kernel Method for Semi-supervised Clustering
  • BookChapter: Diversified SVM Ensembles for Large Data Sets
  • BookChapter: Bayesian Active Learning for Sensitivity Analysis
  • BookChapter: Revisiting Fisher Kernels for Document Similarities
  • BookChapter: Robust Probabilistic Calibration
  • BookChapter: Pertinent Background Knowledge for Learning Protein Grammars
  • BookChapter: EM Algorithm for Symmetric Causal Independence Models
  • BookChapter: Cost-Sensitive Decision Tree Learning for Forensic Classification
  • BookChapter: (Agnostic) PAC Learning Concepts in Higher-Order Logic
  • BookChapter: A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses
  • BookChapter: Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
  • BookChapter: Learning in One-Shot Strategic Form Games
  • BookChapter: Winning the DARPA Grand Challenge
  • BookChapter: Combinatorial Markov Random Fields
  • BookChapter: TildeCRF: Conditional Random Fields for Logical Sequences
  • BookChapter: Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures
  • BookChapter: Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
  • BookChapter: Right of Inference: Nearest Rectangle Learning Revisited
  • BookChapter: Mixtures of Kikuchi Approximations
  • BookChapter: Bayesian Learning with Mixtures of Trees
  • BookChapter: Fast Variational Inference for Gaussian Process Models Through KL-Correction
  • BookChapter: Deconvolutive Clustering of Markov States
  • BookChapter: Margin-Based Active Learning for Structured Output Spaces
  • BookChapter: Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees
  • BookChapter: Learning Stochastic Tree Edit Distance
  • BookChapter: Improving Control-Knowledge Acquisition for Planning by Active Learning
  • BookChapter: Localized Alternative Cluster Ensembles for Collaborative Structuring
  • BookChapter: Active Learning with Irrelevant Examples
  • BookChapter: Case-Based Label Ranking
  • BookChapter: Bagging Using Statistical Queries
  • BookChapter: Spline Embedding for Nonlinear Dimensionality Reduction
  • BookChapter: Missing Data in Kernel PCA
  • BookChapter: To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles
  • BookChapter: Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning
  • BookChapter: Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data
  • BookChapter: On Temporal Evolution in Data Streams
  • BookChapter: The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces
  • BookChapter: Fisher Kernels for Relational Data
  • BookChapter: Making Good Probability Estimates for Regression
  • BookChapter: Distributional Features for Text Categorization
  • BookChapter: Fast Spectral Clustering of Data Using Sequential Matrix Compression
  • BookChapter: Multiple-Instance Learning Via Random Walk
  • BookChapter: Skill Acquisition Via Transfer Learning and Advice Taking
  • BookChapter: Improving the Ranking Performance of Decision Trees
  • BookChapter: Learning to Have Fun
  • BookChapter: On Testing the Missing at Random Assumption
  • BookChapter: Batch Classification with Applications in Computer Aided Diagnosis
  • BookChapter: Ensembles of Nearest Neighbor Forecasts
  • BookChapter: Challenges of Urban Sensing
  • BookChapter: B-Matching for Spectral Clustering
  • BookChapter: Reinforcement Learning for MDPs with Constraints
  • BookChapter: Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions
  • BookChapter: Constant Rate Approximate Maximum Margin Algorithms
  • BookChapter: Learning Process Models with Missing Data
  • BookChapter: Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
  • BookChapter: Evaluating Misclassifications in Imbalanced Data
  • BookChapter: Efficient Inference in Large Conditional Random Fields
  • BookChapter: Boosting in PN Spaces
  • BookChapter: Cascade Evaluation of Clustering Algorithms
  • BookChapter: An Efficient Approximation to Lookahead in Relational Learners
  • BookChapter: Transductive Gaussian Process Regression with Automatic Model Selection
  • BookChapter: Exploiting Extremely Rare Features in Text Categorization
  • BookChapter: Bandit Based Monte-Carlo Planning
  • BookChapter: Efficient Large Scale Linear Programming Support Vector Machines
  • BookChapter: PAC-Learning of Markov Models with Hidden State
  • BookChapter: Bayesian Learning of Markov Network Structure
  • BookChapter: Automatically Evolving Rule Induction Algorithms
  • BookChapter: Efficient Non-linear Control Through Neuroevolution
  • BookChapter: Cost-Sensitive Learning of SVM for Ranking
  • BookChapter: Graph Based Semi-supervised Learning with Sharper Edges
  • BookChapter: Why Is Rule Learning Optimistic and How to Correct It
  • BookChapter: Efficient Prediction-Based Validation for Document Clustering


  • Computer Science
  • Algorithm Analysis And Problem Complexity
  • Mathematical Logic And Formal Languages
  • Database Management
  • Artificial Intelligence (Incl. Robotics)

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    26 TRIPLES      18 PREDICATES      27 URIs      12 LITERALS

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    2 sg:chapterCount 87
    3 sg:copyrightHolder Springer-Verlag Berlin Heidelberg
    4 sg:copyrightYear 2006
    5 sg:ddsId 133207
    6 sg:editionNumber 1
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    9 grid-institutes:grid.5807.a
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    19 sg:language En
    20 sg:license
    21 sg:publisher Springer Berlin Heidelberg
    22 sg:scigraphId c721188d4cd80fb2364cb2782b8635ee
    23 sg:subtitle 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings
    24 sg:title Machine Learning: ECML 2006
    25 rdf:type sg:BookEdition
    26 rdfs:label BookEdition: Machine Learning: ECML 2006

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