Joost N. Kok, Dunja Mladeni?, Stan Matwin, Jacek Koronacki, Andrzej Skowron, Raomon Lopez de Mantaras




Machine Learning: ECML 2007




Springer Berlin Heidelberg

BOOK (manifestation)

  • Book: 978-3-540-74957-8 (Book)
  • Book: 978-3-540-74958-5 (eBook)

  • Related objects


  • Conference: European Conference On Machine Learning


  • BookChapter: Learning to Classify Documents with Only a Small Positive Training Set
  • BookChapter: Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search
  • BookChapter: Classifier Loss Under Metric Uncertainty
  • BookChapter: Probabilistic Models for Action-Based Chinese Dependency Parsing
  • BookChapter: Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
  • BookChapter: Analyzing Co-training Style Algorithms
  • BookChapter: On Pairwise Naive Bayes Classifiers
  • BookChapter: A Simple Lexicographic Ranker and Probability Estimator
  • BookChapter: Avoiding Boosting Overfitting by Removing Confusing Samples
  • BookChapter: Semi-supervised Collaborative Text Classification
  • BookChapter: Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
  • BookChapter: Multi-objective Genetic Programming for Multiple Instance Learning
  • BookChapter: Learning from Relevant Tasks Only
  • BookChapter: Spectral Clustering and Embedding with Hidden Markov Models
  • BookChapter: User Oriented Hierarchical Information Organization and Retrieval
  • BookChapter: K-Means with Large and Noisy Constraint Sets
  • BookChapter: On Minimizing the Position Error in Label Ranking
  • BookChapter: Learning an Outlier-Robust Kalman Filter
  • BookChapter: Probabilistic Explanation Based Learning
  • BookChapter: Learning Partially Observable Markov Models from First Passage Times
  • BookChapter: Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition
  • BookChapter: Level Learning Set: A Novel Classifier Based on Active Contour Models
  • BookChapter: Sequence Labeling with Reinforcement Learning and Ranking Algorithms
  • BookChapter: Statistical Debugging Using Latent Topic Models
  • BookChapter: Shrinkage Estimator for Bayesian Network Parameters
  • BookChapter: On Phase Transitions in Learning Sparse Networks
  • BookChapter: Optimizing Feature Sets for Structured Data
  • BookChapter: Comparing Rule Measures for Predictive Association Rules
  • BookChapter: Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators
  • BookChapter: Source Separation with Gaussian Process Models
  • BookChapter: Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble
  • BookChapter: Hinge Rank Loss and the Area Under the ROC Curve
  • BookChapter: Efficient Pairwise Classification
  • BookChapter: Semi-definite Manifold Alignment
  • BookChapter: An Unsupervised Learning Algorithm for Rank Aggregation
  • BookChapter: Context Sensitive Paraphrasing with a Global Unsupervised Classifier
  • BookChapter: Learning Metrics Between Tree Structured Data: Application to Image Recognition
  • BookChapter: Stepwise Induction of Multi-target Model Trees
  • BookChapter: Adventures in Personalized Information Access
  • BookChapter: Clustering Trees with Instance Level Constraints
  • BookChapter: Imitation Learning Using Graphical Models
  • BookChapter: Decision Tree Instability and Active Learning
  • BookChapter: General Solution for Supervised Graph Embedding
  • BookChapter: Learning Balls of Strings with Correction Queries
  • BookChapter: Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA
  • BookChapter: Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering
  • BookChapter: Bayesian Inference for Sparse Generalized Linear Models
  • BookChapter: Scale-Space Based Weak Regressors for Boosting
  • BookChapter: Discriminative Sequence Labeling by Z-Score Optimization
  • BookChapter: An Improved Model Selection Heuristic for AUC
  • BookChapter: Modeling Highway Traffic Volumes
  • BookChapter: Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
  • BookChapter: Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction
  • BookChapter: Test-Cost Sensitive Classification Based on Conditioned Loss Functions
  • BookChapter: Planning and Learning in Environments with Delayed Feedback
  • BookChapter: Undercomplete Blind Subspace Deconvolution Via Linear Prediction
  • BookChapter: Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning
  • BookChapter: Class Noise Mitigation Through Instance Weighting
  • BookChapter: Weighted Kernel Regression for Predicting Changing Dependencies
  • BookChapter: Random k-Labelsets: An Ensemble Method for Multilabel Classification
  • BookChapter: Safe Q-Learning on Complete History Spaces
  • BookChapter: Principal Component Analysis for Large Scale Problems with Lots of Missing Values
  • BookChapter: Kernel-Based Grouping of Histogram Data
  • BookChapter: Ensembles of Multi-Objective Decision Trees
  • BookChapter: Active Class Selection
  • BookChapter: Graph-Based Domain Mapping for Transfer Learning in General Games
  • BookChapter: Additive Groves of Regression Trees
  • BookChapter: Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures
  • BookChapter: Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models
  • BookChapter: Structure Learning of Probabilistic Relational Models from Incomplete Relational Data
  • BookChapter: Roulette Sampling for Cost-Sensitive Learning
  • BookChapter: Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
  • BookChapter: Dual Strategy Active Learning
  • BookChapter: Approximating Gaussian Processes with ${\cal H}^2$-Matrices
  • BookChapter: Mining Queries
  • BookChapter: The Cost of Learning Directed Cuts
  • BookChapter: Neighborhood-Based Local Sensitivity
  • BookChapter: Learning, Information Extraction and the Web
  • BookChapter: Counter-Example Generation-Based One-Class Classification
  • BookChapter: Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation
  • BookChapter: Efficient Computation of Recursive Principal Component Analysis for Structured Input
  • BookChapter: Policy Gradient Critics


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

  • How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

    Download the RDF metadata as:   json-ld nt turtle xml License info

    26 TRIPLES      17 PREDICATES      27 URIs      12 LITERALS

    Subject Predicate Object
    1 book-editions:1401735b8fc87dc8401156bfb11ab941 sg:bookType Proceedings
    2 sg:chapterCount 82
    3 sg:copyrightHolder Springer-Verlag Berlin Heidelberg
    4 sg:copyrightYear 2007
    5 sg:ddsId 139431
    6 sg:editionNumber 1
    7 sg:hasConference conferences:0bb0521d3af81f0d4aee4d35e9f6d08a
    8 sg:hasContribution contributions:0a792ea599a0995190afe7965a199b14
    9 contributions:289c97e79dbce27720b5268053ddb61c
    10 contributions:2951c92d8993ad651457dfa42c3ef18e
    11 contributions:800df6cbfc9c504175edd44e95be898f
    12 contributions:86c1e6bb8a4aceb5ce4f5282c0c8f9e8
    13 contributions:ecc0e1b114a19ca023cacc841a88cac7
    14 sg:hasProductMarketCode product-market-codes:I
    15 product-market-codes:I16021
    16 product-market-codes:I16048
    17 product-market-codes:I18024
    18 product-market-codes:I21017
    19 sg:language En
    20 sg:license
    21 sg:publisher Springer Berlin Heidelberg
    22 sg:scigraphId 1401735b8fc87dc8401156bfb11ab941
    23 sg:subtitle 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings
    24 sg:title Machine Learning: ECML 2007
    25 rdf:type sg:BookEdition
    26 rdfs:label BookEdition: Machine Learning: ECML 2007

    JSON-LD is a popular JSON format for linked data.

    curl -H 'Accept: application/ld+json' ''

    N-Triples is a line-based linked data format ideal for batch operations .

    curl -H 'Accept: application/n-triples' ''

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' ''

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' ''

    Preview window. Press ESC to close (or click here)