000			03782nam a22004335i 4500
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001			88227
003			ES-MaUEC
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007			cr nn 008mamaa
008			161108s2016 gw | s |||| 0|eng d
020			_a9783319477596
024	7		_a10.1007/978-3-319-47759-6 _2doi
040			_aES-MaUEC
050		4	_aQ325.5 _bH477 2016 EB
100	1		_aHerrera, Francisco. _0http://id.loc.gov/authorities/names/n85336219 _0http://viaf.org/viaf/13445979 _945432
245	1	0	_aMultiple Instance Learning : _bFoundations and Algorithms _cby Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, Sarah Vluymans.
260			_aCham, Switzerland _bSpringer _c2016
300			_a1 recurso en línea (XI, 233 p.) _b46 ilustraciones, 40 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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	7	_aAlgoritmos _2embne _9141162
650		7	_aInteligencia artificial _2embne _9413115
650	0	7	_aProceso digital de imágenes _2embne _9413188
700	1		_aVentura, Sebastián. _999454 _0Local
700	1		_aBello, Rafael _0Local _9101791
700	1		_aCornelis, Chris. _9101792 _0Local
700	1		_aZafra, Amelia _0Local _9101793
700	1		_aSánchez-Tarragó, Dánel. _947318
700	1		_aVluymans, Sarah. _9101794 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-47759-6zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12982313 _b10-10-17 _c08-03-17
942			_2lcc _cLE
945			_aQ325.5 H477 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11604153 _z06-04-17
988			_aEBOOK, EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0