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.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. 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. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.