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Record Nr. |
UNINA9910483503403321 |
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Titolo |
Machine learning and knowledge discovery in databases [[electronic resource] ] : European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010 ; proceedings . Part 1 / / Jose Luis Balcazar ... [et al.[ (eds.) |
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Pubbl/distr/stampa |
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Berlin ; ; New York, : Springer, c2010 |
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ISBN |
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1-280-38918-4 |
9786613567109 |
3-642-15880-3 |
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Edizione |
[1st ed. 2010.] |
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Descrizione fisica |
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1 online resource (XXX, 620 p. 175 illus.) |
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Collana |
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Lecture notes in artificial intelligence ; ; 6321 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Machine learning |
Database management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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