LEADER 06375nam 22008535 450 001 9910484330703321 005 20251226202146.0 010 $a3-540-88411-4 024 7 $a10.1007/978-3-540-88411-8 035 $a(CKB)1000000000490510 035 $a(SSID)ssj0000317233 035 $a(PQKBManifestationID)11267288 035 $a(PQKBTitleCode)TC0000317233 035 $a(PQKBWorkID)10287282 035 $a(PQKB)11579092 035 $a(DE-He213)978-3-540-88411-8 035 $a(MiAaPQ)EBC3063640 035 $a(MiAaPQ)EBC6282929 035 $a(PPN)130185698 035 $a(EXLCZ)991000000000490510 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDiscovery Science $e11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008, Proceedings /$fedited by Jean-Francois Boulicaut, Michael R. Berthold, Tamás Horváth 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XII, 348 p. 96 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5255 300 $aIncludes index. 311 08$a3-540-88410-6 320 $aIncludes bibliographical references and index. 327 $aInvited Papers -- On Iterative Algorithms with an Information Geometry Background -- Visual Analytics: Combining Automated Discovery with Interactive Visualizations -- Some Mathematics Behind Graph Property Testing -- Finding Total and Partial Orders from Data for Seriation -- Computational Models of Neural Representations in the Human Brain -- Learning -- Unsupervised Classifier Selection Based on Two-Sample Test -- An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics -- Learning Model Trees from Data Streams -- Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees -- Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees -- A Comparison between Neural Network Methods for Learning Aggregate Functions -- Feature Selection -- Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns -- Feature Selection in Taxonomies with Applications to Paleontology -- Associations -- Deduction Schemes for Association Rules -- Constructing Iceberg Lattices from Frequent Closures Using Generators -- Discovery Processes -- Learning from Each Other -- Comparative Evaluation of Two Systems for the Visual Navigation of Encyclopedia Knowledge Spaces -- A Framework for Knowledge Discovery in a Society of Agents -- Learning and Chemistry -- Active Learning for High Throughput Screening -- An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules -- Mining Intervals of Graphs to Extract Characteristic Reaction Patterns -- Clustering -- Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations -- Input Noise Robustness and Sensitivity Analysis to Improve Large Datasets Clustering by Using the GRID -- An Integrated Graph and Probability Based Clustering Framework for Sequential Data -- Cluster Analysis in Remote Sensing Spectral Imagery through Graph Representation and Advanced SOM Visualization -- Structured Data -- Mining Unordered Distance-Constrained Embedded Subtrees -- Finding Frequent Patterns from Compressed Tree-Structured Data -- A Modeling Approach Using Multiple Graphs for Semi-Supervised Learning -- Text Analysis -- String Kernels Based on Variable-Length-Don?t-Care Patterns -- Unsupervised Spam Detection by Document Complexity Estimation -- A Probabilistic Neighbourhood Translation Approach for Non-standard Text Categorisation. 330 $aThis book constitutes the refereed proceedings of the 11th International Conference on Discovery Science, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the 19th International Conference on Algorithmic Learning Theory, ALT 2008. The 26 revised long papers presented together with 5 invited papers were carefully reviewed and selected from 58 submissions. The papers address all current issues in the area of development and analysis of methods for intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery. The papers are organized in topical sections on learning, feature selection, associations, discovery processes, learning and chemistry, clustering, structured data, and text analysis. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5255 606 $aArtificial intelligence 606 $aData mining 606 $aDatabase management 606 $aInformation storage and retrieval systems 606 $aInformation technology$xManagement 606 $aSocial sciences$xData processing 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aDatabase Management 606 $aInformation Storage and Retrieval 606 $aComputer Application in Administrative Data Processing 606 $aComputer Application in Social and Behavioral Sciences 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aDatabase management. 615 0$aInformation storage and retrieval systems. 615 0$aInformation technology$xManagement. 615 0$aSocial sciences$xData processing. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aComputer Application in Administrative Data Processing. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a501 686 $a54.72$2bcl 702 $aBoulicaut$b Jean-Francois$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBerthold$b Michael R$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHorváth$b Tamás$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Conference on Discovery Science 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484330703321 996 $aDiscovery Science$92968615 997 $aUNINA