Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I / / edited by José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010
• Edizione: 1st ed. 2010.
• Descrizione fisica: 1 online resource (XXX, 620 p. 175 illus.)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Data structures (Computer science); Application software; Information storage and retrieval; Database management; Data mining; Artificial Intelligence; Data Structures and Information Theory; Information Systems Applications (incl. Internet); Information Storage and Retrieval; Database Management; Data Mining and Knowledge Discovery
• Persona (resp. second.): BalcázarJosé L; BonchiFrancesco; GionisAristides; SebagMichèle
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Titolo autorizzato: Machine Learning and Knowledge Discovery in Databases
• ISBN: 1-280-38918-4; 9786613567109; 3-642-15880-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996466569903316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 6321