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Produktbild: Machine Learning and Knowledge Discovery in Databases
Band 6321

Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I

Aus der Reihe Lecture Notes in Computer Science

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.09.2010

Abbildungen

XXX, 620 p. 175 illus.

Herausgeber

José L. Balcázar + weitere

Verlag

Springer Berlin

Seitenzahl

620

Sprache

Englisch

ISBN

978-3-642-15879-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.09.2010

Abbildungen

XXX, 620 p. 175 illus.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

620

Sprache

Englisch

ISBN

978-3-642-15879-7

Herstelleradresse

Springer-Verlag GmbH
Heidelberger Platz 3
14197 Berlin
Deutschland
Email: sdc-bookservice@springer.com
Url: www.springer.com
Telephone: +49 30 827870
Fax: +49 30 8214091

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  • Produktbild: Machine Learning and Knowledge Discovery in Databases
  • 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.