Machine Learning and Knowledge Discovery in Databases

ISBN-13: 9783540874805 / Angielski / Miękka / 2008 / 728 str.

Walter Daelemans

Opis

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Spis treści

Regular Papers.- Exceptional Model Mining.- A Joint Topic and Perspective Model for Ideological Discourse.- Effective Pruning Techniques for Mining Quasi-Cliques.- Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain.- Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs.- A New Natural Policy Gradient by Stationary Distribution Metric.- Towards Machine Learning of Grammars and Compilers of Programming Languages.- Improving Classification with Pairwise Constraints: A Margin-Based Approach.- Metric Learning: A Support Vector Approach.- Support Vector Machines, Data Reduction, and Approximate Kernel Matrices.- Mixed Bregman Clustering with Approximation Guarantees.- Hierarchical, Parameter-Free Community Discovery.- A Genetic Algorithm for Text Classification Rule Induction.- Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness.- Kernel-Based Inductive Transfer.- State-Dependent Exploration for Policy Gradient Methods.- Client-Friendly Classification over Random Hyperplane Hashes.- Large-Scale Clustering through Functional Embedding.- Clustering Distributed Sensor Data Streams.- A Novel Scalable and Data Efficient Feature Subset Selection Algorithm.- Robust Feature Selection Using Ensemble Feature Selection Techniques.- Effective Visualization of Information Diffusion Process over Complex Networks.- Actively Transfer Domain Knowledge.- A Unified View of Matrix Factorization Models.- Parallel Spectral Clustering.- Classification of Multi-labeled Data: A Generative Approach.- Pool-Based Agnostic Experiment Design in Linear Regression.- Distribution-Free Learning of Bayesian Network Structure.- Assessing Nonlinear Granger Causality from Multivariate Time Series.- Clustering Via Local Regression.- Decomposable Families of Itemsets.- Transferring Instances for Model-Based Reinforcement Learning.- A Simple Model for Sequences of Relational State Descriptions.- Semi-Supervised Boosting for Multi-Class Classification.- A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis.- Transferred Dimensionality Reduction.- Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model.- Estimating Sales Opportunity Using Similarity-Based Methods.- Learning MDP Action Models Via Discrete Mixture Trees.- Continuous Time Bayesian Networks for Host Level Network Intrusion Detection.- Data Streaming with Affinity Propagation.- Semi-supervised Discriminant Analysis Via CCCP.- Demo Papers.- A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains.- Pleiades: Subspace Clustering and Evaluation.- SEDiL: Software for Edit Distance Learning.- Monitoring Patterns through an Integrated Management and Mining Tool.- A Knowledge-Based Digital Dashboard for Higher Learning Institutions.- SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model.