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Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I - Erstausgabe
2010, ISBN: 364215879X
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2010, ISBN: 9783642158797
European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2010
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2010, ISBN: 9783642158797
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2010, ISBN: 9783642158797
European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I, Buch, Softcover, The European Conference on Machine Learning and Principles and Practice… Mehr…
2010, ISBN: 9783642158797
The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight c… Mehr…
Machine Learning and Knowledge Discovery in Databases: European Conference, Ecml Pkdd 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I - Erstausgabe
2010
ISBN: 364215879X
Taschenbuch
[EAN: 9783642158797], Neubuch, [PU: Springer-Verlag New York Inc], 1st edition. edition. 620 pages. 9.10x6.10x1.00 inches. In Stock., Books
2010, ISBN: 9783642158797
European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I, Buch, Softcover, [PU: Springer Berlin], Springer Berlin, 2010
2010, ISBN: 9783642158797
PAPERBACK, Neubuch, [PU: Springer]
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Detailangaben zum Buch - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part
EAN (ISBN-13): 9783642158797
ISBN (ISBN-10): 364215879X
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2010
Herausgeber: Springer Berlin Heidelberg Core >1 >T
620 Seiten
Gewicht: 0,900 kg
Sprache: eng/Englisch
Buch in der Datenbank seit 2009-03-28T05:02:47+01:00 (Vienna)
Detailseite zuletzt geändert am 2024-03-11T13:31:49+01:00 (Vienna)
ISBN/EAN: 9783642158797
ISBN - alternative Schreibweisen:
3-642-15879-X, 978-3-642-15879-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: aristides, josé, aris, arist, michel jose, gioni, sebag
Titel des Buches: barcelona, machine learning, knowledge discovery databases
Daten vom Verlag:
Autor/in: José L. Balcázar; Francesco Bonchi; Aristides Gionis; Michèle Sebag
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I
Verlag: Springer; Springer Berlin
620 Seiten
Erscheinungsjahr: 2010-09-13
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XXX, 620 p. 175 illus.
BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Informatik; bio-informatics; classification; clustering; data analysis; data mining; dimensionality reduction; information retrieval; knowledge; knowledge discovery; learning; machine learning; multi-agent systems; semantic web; social networks; web mining; Artificial Intelligence; Data Structures and Information Theory; Computer and Information Systems Applications; Information Storage and Retrieval; Database Management; Data Mining and Knowledge Discovery; Algorithmen und Datenstrukturen; Informationstheorie; Angewandte Informatik; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Datenbanken; Data Mining; Wissensbasierte Systeme, Expertensysteme; EA
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.Unique visibility State-of-the-art survey Fast-track conference proceedings
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