000 | 05953nam a22006615i 4500 | ||
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001 | 978-3-642-15880-3 | ||
003 | DE-He213 | ||
005 | 20240730193116.0 | ||
007 | cr nn 008mamaa | ||
008 | 100817s2010 gw | s |||| 0|eng d | ||
020 |
_a9783642158803 _9978-3-642-15880-3 |
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024 | 7 |
_a10.1007/978-3-642-15880-3 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aMachine Learning and Knowledge Discovery in Databases _h[electronic resource] : _bEuropean Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings, Part I / _cedited by José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag. |
250 | _a1st ed. 2010. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2010. |
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300 |
_aXXX, 620 p. 175 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v6321 |
|
505 | 0 | _aInvited 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. | |
650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aData structures (Computer science). _98188 |
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650 | 0 |
_aInformation theory. _914256 |
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650 | 0 |
_aApplication software. _9152469 |
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650 | 0 |
_aInformation storage and retrieval systems. _922213 |
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650 | 0 |
_aDatabase management. _93157 |
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650 | 0 |
_aData mining. _93907 |
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650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aData Structures and Information Theory. _931923 |
650 | 2 | 4 |
_aComputer and Information Systems Applications. _9152470 |
650 | 2 | 4 |
_aInformation Storage and Retrieval. _923927 |
650 | 2 | 4 |
_aDatabase Management. _93157 |
650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _9152471 |
700 | 1 |
_aBalcázar, José L. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9152472 |
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700 | 1 |
_aBonchi, Francesco. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9152473 |
|
700 | 1 |
_aGionis, Aristides. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9152474 |
|
700 | 1 |
_aSebag, Michèle. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9152475 |
|
710 | 2 |
_aSpringerLink (Online service) _9152476 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783642158797 |
776 | 0 | 8 |
_iPrinted edition: _z9783642158810 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v6321 _9152477 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-642-15880-3 |
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