Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings (Lecture Notes in Computer . / Lecture Notes in Artificial Intelligence)

Boulicaut, Jean-Francois [Editor]; Esposito, Floriana [Editor]; Giannotti, Fosca [Editor]; Pedreschi, Dino [Editor];

ISBN 10: 3540231056 / ISBN 13: 9783540231059

Editore: Springer, 2008

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Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding.

Codice articolo UTD14a-0542

Dati bibliografici

• Titolo: Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings (Lecture Notes in Computer . / Lecture Notes in Artificial Intelligence)
• Editore: Springer
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540231056
• ISBN 13: 9783540231059
• Rilegatura: Paperback
• Condizione: Very Good
• Peso: 31 once

Descrizione articolo

Riassunto

The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20 24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).

Contenuti

Invited Papers.- Random Matrices in Data Analysis.- Data Privacy.- Breaking Through the Syntax Barrier: Searching with Entities and Relations.- Real-World Learning with Markov Logic Networks.- Strength in Diversity: The Advance of Data Analysis.- Contributed Papers.- Filtered Reinforcement Learning.- Applying Support Vector Machines to Imbalanced Datasets.- Sensitivity Analysis of the Result in Binary Decision Trees.- A Boosting Approach to Multiple Instance Learning.- An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic Games.- Learning from Message Pairs for Automatic Email Answering.- Concept Formation in Expressive Description Logics.- Multi-level Boundary Classification for Information Extraction.- An Analysis of Stopping and Filtering Criteria for Rule Learning.- Adaptive Online Time Allocation to Search Algorithms.- Model Approximation for HEXQ Hierarchical Reinforcement Learning.- Iterative Ensemble Classification for Relational Data: A Case Study of Semantic Web Services.- Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics.- Experiments in Value Function Approximation with Sparse Support Vector Regression.- Constructive Induction for Classifying Time Series.- Fisher Kernels for Logical Sequences.- The Enron Corpus: A New Dataset for Email Classification Research.- Margin Maximizing Discriminant Analysis.- Multi-objective Classification with Info-Fuzzy Networks.- Improving Progressive Sampling via Meta-learning on Learning Curves.- Methods for Rule Conflict Resolution.- An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk.- Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection.- Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework.- Justification-Based Selection of Training Examples for Case Base Reduction.- Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers.- Feature Selection Filters Based on the Permutation Test.- Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning.- Improving Random Forests.- The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering.- Using String Kernels to Identify Famous Performers from Their Playing Style.- Associative Clustering.- Learning to Fly Simple and Robust.- Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data.- Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees.- Inducing Polynomial Equations for Regression.- Efficient Hyperkernel Learning Using Second-Order Cone Programming.- Effective Voting of Heterogeneous Classifiers.- Convergence and Divergence in Standard and Averaging Reinforcement Learning.- Document Representation for One-Class SVM.- Naive Bayesian Classifiers for Ranking.- Conditional Independence Trees.- Exploiting Unlabeled Data in Content-Based Image Retrieval.- Population Diversity in Permutation-Based Genetic Algorithm.- Simultaneous Concept Learning of Fuzzy Rules.- Posters.- SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data.- Estimating Attributed Central Orders.- Batch Reinforcement Learning with State Importance.- Explicit Local Models: Towards “Optimal” Optimization Algorithms.- An Intelligent Model for the Signorini Contact Problem in Belt Grinding Processes.- Cluster-Grouping: From Subgroup Discovery to Clustering.