Blick ins Buch

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part III

José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag (Herausgeber)

Buch | Softcover

XXII, 632 Seiten

2010
Springer Berlin (Verlag)
978-3-642-15938-1 (ISBN)

Lese- und Medienproben

Blick ins Buch (midvox)

Artikel merken

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.

Regular Papers.- Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations.- Unsupervised Trajectory Sampling.- Fast Extraction of Locally Optimal Patterns Based on Consistent Pattern Function Variations.- Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models.- Learning with Ensembles of Randomized Trees : New Insights.- Entropy and Margin Maximization for Structured Output Learning.- Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms.- Adapting Decision DAGs for Multipartite Ranking.- Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs.- Modeling Relations and Their Mentions without Labeled Text.- An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery.- Selecting Information Diffusion Models over Social Networks for Behavioral Analysis.- Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach.- Online Structural Graph Clustering Using Frequent Subgraph Mining.- Large-Scale Support Vector Learning with Structural Kernels.- Synchronization Based Outlier Detection.- Laplacian Spectrum Learning.- k-Version-Space Multi-class Classification Based on k-Consistency Tests.- Complexity Bounds for Batch Active Learning in Classification.- Semi-supervised Projection Clustering with Transferred Centroid Regularization.- Permutation Testing Improves Bayesian Network Learning.- Example-dependent Basis Vector Selection for Kernel-Based Classifiers.- Surprising Patterns for the Call Duration Distribution of Mobile Phone Users.- Variational Bayesian Mixture of Robust CCA Models.- Adverse Drug Reaction Mining in Pharmacovigilance Data Using Formal Concept Analysis.- Topic Models Conditioned on Relations.- Shift-Invariant Grouped Multi-task Learning for Gaussian Processes.- Nonparametric Bayesian Clustering Ensembles.- Directed Graph Learning via High-Order Co-linkage Analysis.- Incorporating Domain Models into Bayesian Optimization for RL.- Efficient and Numerically Stable Sparse Learning.- Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes.- Many-to-Many Graph Matching: A Continuous Relaxation Approach.- Competitive Online Generalized Linear Regression under Square Loss.- Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning.- Fast, Effective Molecular Feature Mining by Local Optimization.- Demo Papers.- AnswerArt - Contextualized Question Answering.- Real-Time News Recommender System.- CET: A Tool for Creative Exploration of Graphs.- NewsGist: A Multilingual Statistical News Summarizer.- QUEST: Query Expansion Using Synonyms over Time.- Flu Detector - Tracking Epidemics on Twitter.- X-SDR: An Extensible Experimentation Suite for Dimensionality Reduction.- SOREX: Subspace Outlier Ranking Exploration Toolkit.- KDTA: Automated Knowledge-Driven Text Annotation.- Detecting Events in a Million New York Times Articles.- Experience STORIES: A Visual News Search and Summarization System.- Exploring Real Mobility Data with M-Atlas.

Erscheint lt. Verlag	13.9.2010
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XXII, 632 p. 183 illus.
Verlagsort	Berlin
Sprache	englisch
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	bio-informatics • classification • Clustering • Data Analysis • Databases • Data Mining • dimensionality reduction • graph mining • Information Retrieval • Knowledge • Knowledge Discovery • learning • machine learning • Multi-agent Systems • semantic web • Social Networks • Web mining
ISBN-10	3-642-15938-9 / 3642159389
ISBN-13	978-3-642-15938-1 / 9783642159381
Zustand	Neuware