000			03930nam a22005655i 4500
001			978-3-319-47759-6
003			DE-He213
005			20180206183019.0
007			cr nn 008mamaa
008			161108s2016 gw | s |||| 0|eng d
020			_a9783319477596 _9978-3-319-47759-6
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[recurso electrónico] : _bFoundations and Algorithms / _cby Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, 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, Sebastián. _eauthor.
700	1		_aBello, Rafael. _eauthor.
700	1		_aCornelis, Chris. _eauthor.
700	1		_aZafra, Amelia. _eauthor.
700	1		_aSánchez-Tarragó, Dánel. _eauthor.
700	1		_aVluymans, Sarah. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319477589
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-47759-6
912			_aZDB-2-SCS
942			_cLIBRO_ELEC
999			_c226369 _d226369