LEADER 06079nam 22007815 450 001 9910483133503321 005 20200705120230.0 010 $a3-319-24282-2 024 7 $a10.1007/978-3-319-24282-8 035 $a(CKB)4340000000001121 035 $a(SSID)ssj0001585048 035 $a(PQKBManifestationID)16265769 035 $a(PQKBTitleCode)TC0001585048 035 $a(PQKBWorkID)14866195 035 $a(PQKB)11609617 035 $a(DE-He213)978-3-319-24282-8 035 $a(MiAaPQ)EBC6219445 035 $a(MiAaPQ)EBC5587662 035 $a(Au-PeEL)EBL5587662 035 $a(OCoLC)932169244 035 $a(PPN)190528486 035 $a(EXLCZ)994340000000001121 100 $a20151003d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDiscovery Science $e18th International Conference, DS 2015, Banff, AB, Canada, October 4-6, 2015. Proceedings /$fedited by Nathalie Japkowicz, Stan Matwin 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XV, 342 p. 96 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence ;$v9356 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-24281-4 327 $aBilinear Prediction using Low Rank Models -- Finding Hidden Structure in Data with Tensor Decompositions -- Turning Prediction Tools Into Decision Tools -- Overcoming obstacles to the adoption of machine learning by domain Experts -- Resolution transfer in cancer classification based on amplification patterns -- Very Short-Term Wind Speed Forecasting using Spatio-Temporal Lazy Learning -- Discovery of Parameters for Animation of Midge Swarms -- No Sentiment is an Island: Author's activity and sentiments transactions in sentiment classification -- Active Learning for Classifying Template Matches in Historical Maps -- An evaluation of score descriptors combined with non-linear models of expressive dynamics in music -- Geo-Coordinated Parallel Coordinates (GCPC): A Case Study of Environmental Data Analysis -- Generalized Shortest Path Kernel on Graphs -- Ensembles of extremely randomized trees for multi-target regression -- Clustering-Based Optimised Probabilistic Active Learning (COPAL) -- Predictive Analysis on Tracking Emails for Targeted Marketing -- Semi-supervised Learning for Stream Recommender Systems -- Detecting Transmembrane Proteins Using Decision Trees -- Change point detection for information diffusion tree -- Multi-label Classification via Multi-target Regression on Data Streams -- Periodical Skeletonization for Partially Periodic Pattern Mining -- Predicting Drugs Adverse Side-Effects using a recommender-system -- Dr. Inventor Framework: extracting structured information from scientific publications -- Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel -- Hierarchical Multidimensional Classification of web documents with MultiWebClass -- Evaluating the Effectiveness of Hashtags as Predictors of the Sentiment of Tweets -- On the Feasibility of Discovering Meta-Patterns from a Data Ensemble -- An Algorithm for Influence Maximization in a Two-Terminal Series -- Parallel Graph and Its Application to a Real Network -- Benchmarking Stream Clustering for Churn Detection in Dynamic Networks -- Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface -- KeCo: Kernel-based Online Co-agreement Algorithm -- Tree PCA for Extracting Dominant Substructures from Labeled Rooted Trees -- Enumerating Maximal Clique Sets with Pseudo-Clique Constraint. 330 $aThis book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions.  The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive. 410 0$aLecture Notes in Artificial Intelligence ;$v9356 606 $aArtificial intelligence 606 $aData mining 606 $aInformation storage and retrieval 606 $aDatabase management 606 $aAlgorithms 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aInformation storage and retrieval. 615 0$aDatabase management. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a004 702 $aJapkowicz$b Nathalie$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMatwin$b Stan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483133503321 996 $aDiscovery Science$92968615 997 $aUNINA