04452nam 22005895 450 991015044630332120200702214402.03-319-47759-510.1007/978-3-319-47759-6(CKB)3710000000943232(DE-He213)978-3-319-47759-6(MiAaPQ)EBC4737085(PPN)197141358(EXLCZ)99371000000094323220161108d2016 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMultiple Instance Learning[electronic resource] Foundations and Algorithms /by Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, Sarah Vluymans1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XI, 233 p. 46 illus., 40 illus. in color.) 3-319-47758-7 Includes bibliographical references at the end of each chapters.Introduction -- 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.This 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.Artificial intelligenceOptical data processingAlgorithmsArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Artificial intelligence.Optical data processing.Algorithms.Artificial Intelligence.Image Processing and Computer Vision.Algorithm Analysis and Problem Complexity.006.3Herrera Franciscoauthttp://id.loc.gov/vocabulary/relators/aut426940Ventura Sebastiánauthttp://id.loc.gov/vocabulary/relators/autBello Rafaelauthttp://id.loc.gov/vocabulary/relators/autCornelis Chrisauthttp://id.loc.gov/vocabulary/relators/autZafra Ameliaauthttp://id.loc.gov/vocabulary/relators/autSánchez-Tarragó Dánelauthttp://id.loc.gov/vocabulary/relators/autVluymans Sarahauthttp://id.loc.gov/vocabulary/relators/autBOOK9910150446303321Multiple Instance Learning2158936UNINA