Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part by José L. BalcázarMachine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part by José L. Balcázar

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010…

byJosé L. BalcázarEditorFrancesco Bonchi, Aristides Gionis

Paperback | September 13, 2010

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The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010, was held in Barcelona, September 20-24, 2010, consolidating the long junction between the European Conference on Machine Learning (of which the ?rst instance as European wo- shop dates back to 1986) and Principles and Practice of Knowledge Discovery in Data Bases (of which the ?rst instance dates back to 1997). Since the two conferences were ?rst collocated in 2001, both machine learning and data m- ing communities have realized how each discipline bene?ts from the advances, and participates to de?ning the challenges, of the sister discipline. Accordingly, a single ECML PKDD Steering Committee gathering senior members of both communities was appointed in 2008. In 2010, as in previous years, ECML PKDD lasted from Monday to F- day. It involved six plenary invited talks, by Christos Faloutsos, Jiawei Han, Hod Lipson, Leslie Pack Kaelbling, Tomaso Poggio, and Jur ¨ gen Schmidhuber, respectively. Monday and Friday were devoted to workshops and tutorials, or- nized and selected by Colin de la Higuera and Gemma Garriga.Continuing from ECML PKDD 2009, an industrial session managed by Taneli Mielikainen and Hugo Zaragoza welcomed distinguished speakers from the ML and DM ind- try: Rakesh Agrawal, Mayank Bawa, Ignasi Belda, Michael Berthold, Jos´eLuis Fl´ orez,ThoreGraepel,andAlejandroJaimes.Theconferencealsofeaturedad- coverychallenge,organizedbyAndr´ asBenczur ´ ,CarlosCastillo,Zolt´ anGyon ¨ gyi, and Julien Masan` es.
Title:Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010…Format:PaperbackDimensions:518 pagesPublished:September 13, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:364215882X

ISBN - 13:9783642158827

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Table of Contents

Regular Papers.- Bayesian Knowledge Corroboration with Logical Rules and User Feedback.- Learning an Affine Transformation for Non-linear Dimensionality Reduction.- NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification.- Hidden Conditional Ordinal Random Fields for Sequence Classification.- A Unifying View of Multiple Kernel Learning.- Evolutionary Dynamics of Regret Minimization.- Recognition of Instrument Timbres in Real Polytimbral Audio Recordings.- Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks.- Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction.- Online Knowledge-Based Support Vector Machines.- Learning with Randomized Majority Votes.- Exploration in Relational Worlds.- Efficient Confident Search in Large Review Corpora.- Learning to Tag from Open Vocabulary Labels.- A Robustness Measure of Association Rules.- Automatic Model Adaptation for Complex Structured Domains.- Collective Traffic Forecasting.- On Detecting Clustered Anomalies Using SCiForest.- Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier.- Online Learning in Adversarial Lipschitz Environments.- Summarising Data by Clustering Items.- Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space.- Latent Structure Pattern Mining.- First-Order Bayes-Ball.- Learning from Demonstration Using MDP Induced Metrics.- Demand-Driven Tag Recommendation.- Solving Structured Sparsity Regularization with Proximal Methods.- Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.- Improved MinMax Cut Graph Clustering with Nonnegative Relaxation.- Integrating Constraint Programming and Itemset Mining.- Topic Modeling for Personalized Recommendation of Volatile Items.- Conditional Ranking on Relational Data.