LEADER 04452nam 22005895 450 001 9910150446303321 005 20200702214402.0 010 $a3-319-47759-5 024 7 $a10.1007/978-3-319-47759-6 035 $a(CKB)3710000000943232 035 $a(DE-He213)978-3-319-47759-6 035 $a(MiAaPQ)EBC4737085 035 $a(PPN)197141358 035 $a(EXLCZ)993710000000943232 100 $a20161108d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiple Instance Learning$b[electronic resource] $eFoundations and Algorithms /$fby Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, Sarah Vluymans 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XI, 233 p. 46 illus., 40 illus. in color.) 311 $a3-319-47758-7 320 $aIncludes bibliographical references at the end of each chapters. 327 $aIntroduction -- Multiple Instance Learning -- Multi-Instance Classification -- Instance-Based Classification Methods -- Bag-Based Classification Methods -- Multi-Instance Regression -- Unsupervised Multiple Instance Learning -- Data Reduction -- Imbalance Multi-Instance Data -- Multiple Instance Multiple Label Learning. 330 $aThis book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools. 606 $aArtificial intelligence 606 $aOptical data processing 606 $aAlgorithms 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.3 700 $aHerrera$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut$0426940 702 $aVentura$b Sebastián$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBello$b Rafael$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCornelis$b Chris$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZafra$b Amelia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSánchez-Tarragó$b Dánel$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aVluymans$b Sarah$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910150446303321 996 $aMultiple Instance Learning$92158936 997 $aUNINA