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.

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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.

Inhalt
Invited Talks (Abstracts).- Mining Billion-Node Graphs: Patterns, Generators and Tools.- Structure Is Informative: On Mining Structured Information Networks.- Intelligent Interaction with the Real World.- Mining Experimental Data for Dynamical Invariants - From Cognitive Robotics to Computational Biology.- Hierarchical Learning Machines and Neuroscience of Visual Cortex.- Formal Theory of Fun and Creativity.- Regular Papers.- Porting Decision Tree Algorithms to Multicore Using FastFlow.- On Classifying Drifting Concepts in P2P Networks.- A Unified Approach to Active Dual Supervision for Labeling Features and Examples.- Vector Field Learning via Spectral Filtering.- Weighted Symbols-Based Edit Distance for String-Structured Image Classification.- A Concise Representation of Association Rules Using Minimal Predictive Rules.- Euclidean Distances, Soft and Spectral Clustering on Weighted Graphs.- Adaptive Parallel/Serial Sampling Mechanisms for Particle Filtering in Dynamic Bayesian Networks.- Leveraging Bagging for Evolving Data Streams.- ITCH: Information-Theoretic Cluster Hierarchies.- Coniunge et Impera: Multiple-Graph Mining for Query-Log Analysis.- Process Mining Meets Abstract Interpretation.- Smarter Sampling in Model-Based Bayesian Reinforcement Learning.- Predicting Partial Orders: Ranking with Abstention.- Predictive Distribution Matching SVM for Multi-domain Learning.- Kantorovich Distances between Rankings with Applications to Rank Aggregation.- Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition.- Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss.- Clustering Vessel Trajectories with Alignment Kernels under Trajectory Compression.- Adaptive Bases for Reinforcement Learning.- Constructing Nonlinear Discriminants from Multiple Data Views.- Learning Algorithms for Link Prediction Based on Chance Constraints.- Sparse Unsupervised Dimensionality Reduction Algorithms.- Asking Generalized Queries to Ambiguous Oracle.- Analysis of Large Multi-modal Social Networks: Patterns and a Generator.- A Cluster-Level Semi-supervision Model for Interactive Clustering.- Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs.- Induction of Concepts in Web Ontologies through Terminological Decision Trees.- Classification with Sums of Separable Functions.- Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information.- Bagging for Biclustering: Application to Microarray Data.- Hub Gene Selection Methods for the Reconstruction of Transcription Networks.- Expectation Propagation for Bayesian Multi-task Feature Selection.- Graphical Multi-way Models.- Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval.- Graph Regularized Transductive Classification on Heterogeneous Information Networks.- Temporal Maximum Margin Markov Network.-Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-Like Exploration.