Al?pio M?rio Jorge, Rui Camacho, Jo?o Gama, Lu?s Torgo, Pavel B. Brazdil




Machine Learning: ECML 2005




Springer Berlin Heidelberg

BOOK (manifestation)

  • Book: 978-3-540-31692-3 (eBook)
  • Book: 978-3-540-29243-2 (Book)

  • Related objects


  • University Of Porto


  • Conference: European Conference On Machine Learning


  • BookChapter: Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities
  • BookChapter: On Applying Tabling to Inductive Logic Programming
  • BookChapter: Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another
  • BookChapter: Mode Directed Path Finding
  • BookChapter: Learning from Positive and Unlabeled Examples with Different Data Distributions
  • BookChapter: Ensemble Learning with Supervised Kernels
  • BookChapter: Margin-Sparsity Trade-Off for the Set Covering Machine
  • BookChapter: A Comparison of Approaches for Learning Probability Trees
  • BookChapter: Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam
  • BookChapter: Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing
  • BookChapter: An Optimal Best-First Search Algorithm for Solving Infinite Horizon DEC-POMDPs
  • BookChapter: An Integrated Approach to Learning Bayesian Networks of Rules
  • BookChapter: Learning to Complete Sentences
  • BookChapter: Training Support Vector Machines with Multiple Equality Constraints
  • BookChapter: Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions
  • BookChapter: Robust Bayesian Linear Classifier Ensembles
  • BookChapter: Active Learning in Partially Observable Markov Decision Processes
  • BookChapter: $\mathcal{U}$-Likelihood and $\mathcal{U}$-Updating Algorithms: Statistical Inference in Latent Variable Models
  • BookChapter: The Huller: A Simple and Efficient Online SVM
  • BookChapter: Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce
  • BookChapter: Natural Actor-Critic
  • BookChapter: Two Contributions of Constraint Programming to Machine Learning
  • BookChapter: MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition
  • BookChapter: Strategy Learning for Reasoning Agents
  • BookChapter: Infinite Ensemble Learning with Support Vector Machines
  • BookChapter: Hybrid Algorithms with Instance-Based Classification
  • BookChapter: Towards Finite-Sample Convergence of Direct Reinforcement Learning
  • BookChapter: Multi-armed Bandit Algorithms and Empirical Evaluation
  • BookChapter: On the LearnAbility of Abstraction Theories from Observations for Relational Learning
  • BookChapter: Analysis of Generic Perceptron-Like Large Margin Classifiers
  • BookChapter: Fast Non-negative Dimensionality Reduction for Protein Fold Recognition
  • BookChapter: Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm
  • BookChapter: Statistical Relational Learning: An Inductive Logic Programming Perspective
  • BookChapter: Counting Positives Accurately Despite Inaccurate Classification
  • BookChapter: Machine Learning of Plan Robustness Knowledge About Instances
  • BookChapter: Annealed Discriminant Analysis
  • BookChapter: Inducing Hidden Markov Models to Model Long-Term Dependencies
  • BookChapter: Model Selection in Omnivariate Decision Trees
  • BookChapter: Independent Subspace Analysis on Innovations
  • BookChapter: On Discriminative Joint Density Modeling
  • BookChapter: Data Streams and Data Synopses for Massive Data Sets (Invited Talk)
  • BookChapter: Classification with Maximum Entropy Modeling of Predictive Association Rules
  • BookChapter: A Similar Fragments Merging Approach to Learn Automata on Proteins
  • BookChapter: Learning (k,l)-Contextual Tree Languages for Information Extraction
  • BookChapter: Active Learning for Probability Estimation Using Jensen-Shannon Divergence
  • BookChapter: Kernel Basis Pursuit
  • BookChapter: Clustering and Metaclustering with Nonnegative Matrix Decompositions
  • BookChapter: Data Analysis in the Life Sciences — Sparking Ideas —
  • BookChapter: An Evolutionary Function Approximation Approach to Compute Prediction in XCSF
  • BookChapter: Learning Models of Relational Stochastic Processes
  • BookChapter: A Kernel Between Unordered Sets of Data: The Gaussian Mixture Approach
  • BookChapter: A Model Based Method for Automatic Facial Expression Recognition
  • BookChapter: Machine Learning for Natural Language Processing (and Vice Versa?)
  • BookChapter: Combining Bias and Variance Reduction Techniques for Regression Trees
  • BookChapter: Multi-view Discriminative Sequential Learning
  • BookChapter: Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence
  • BookChapter: Network Game and Boosting
  • BookChapter: A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files
  • BookChapter: Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method
  • BookChapter: Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes
  • BookChapter: Efficient Case Based Feature Construction
  • BookChapter: Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers
  • BookChapter: Estimation of Mixture Models Using Co-EM
  • BookChapter: Recent Advances in Mining Time Series Data
  • BookChapter: Fitting the Smallest Enclosing Bregman Ball
  • BookChapter: Error-Sensitive Grading for Model Combination
  • BookChapter: Classification of Ordinal Data Using Neural Networks
  • BookChapter: A Distance-Based Approach for Action Recommendation
  • BookChapter: A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems
  • BookChapter: Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
  • BookChapter: Model-Based Online Learning of POMDPs
  • BookChapter: Severe Class Imbalance: Why Better Algorithms Aren’t the Answer
  • BookChapter: Similarity-Based Alignment and Generalization
  • BookChapter: Simple Test Strategies for Cost-Sensitive Decision Trees
  • BookChapter: Nonrigid Embeddings for Dimensionality Reduction
  • BookChapter: Rotational Prior Knowledge for SVMs
  • BookChapter: Nonnegative Lagrangian Relaxation of K-Means and Spectral Clustering
  • BookChapter: Learning and Classifying Under Hard Budgets


  • 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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    21 sg:publisher Springer Berlin Heidelberg
    22 sg:scigraphId 9cc5bc99b6926fd7c1ff622bbe2bcc19
    23 sg:subtitle 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings
    24 sg:title Machine Learning: ECML 2005
    25 rdf:type sg:BookEdition
    26 rdfs:label BookEdition: Machine Learning: ECML 2005

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