000 03876nam a22005655i 4500
001 978-3-319-47759-6
003 DE-He213
005 20200421112046.0
007 cr nn 008mamaa
008 161108s2016 gw | s |||| 0|eng d
020 _a9783319477596
_9978-3-319-47759-6
024 7 _a10.1007/978-3-319-47759-6
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aHerrera, Francisco.
_eauthor.
245 1 0 _aMultiple Instance Learning
_h[electronic resource] :
_bFoundations and Algorithms /
_cby Francisco Herrera, Sebasti�an Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, D�anel S�anchez-Tarrag�o, Sarah Vluymans.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXI, 233 p. 46 illus., 40 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
700 1 _aVentura, Sebasti�an.
_eauthor.
700 1 _aBello, Rafael.
_eauthor.
700 1 _aCornelis, Chris.
_eauthor.
700 1 _aZafra, Amelia.
_eauthor.
700 1 _aS�anchez-Tarrag�o, D�anel.
_eauthor.
700 1 _aVluymans, Sarah.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319477589
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-47759-6
912 _aZDB-2-SCS
942 _cEBK
999 _c56956
_d56956