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008			160425s2016 gw | s |||| 0|eng d
020			_a9783319303673
040			_aES-MaUEC _bspa
050		4	_aQ325.5 _b.I65 2016 EB
082	0	4	_a006.3
100	1		_aIonescu, Radu Tudor _0Local _1http://viaf.org/viaf/1380146461420427730206 _998867
245	1	0	_aKnowledge Transfer between Computer Vision and Text Mining : _bSimilarity-based Learning Approaches _cby Radu Tudor Ionescu, Marius Popescu
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXIV, 250 páginas) _b42 ilustraciones, 33 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in Computer Vision and Pattern Recognition _x2191-6586
505	0		_aMotivation and Overview -- Learning Based on Similarity -- Part I: Knowledge Transfer from Text Mining to Computer Vision -- State of the Art Approaches for Image Classification -- Local Displacement Estimation of Image Patches and Textons -- Object Recognition with the Bag of Visual Words Model -- Part II: Knowledge Transfer from Computer Vision to Text Mining -- State of the Art Approaches for String and Text Analysis -- Local Rank Distance -- Native Language Identification with String Kernels -- Spatial Information in Text Categorization -- Conclusions.
520			_aThis ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning techniques founded on this approach. Topics and features: Describes a variety of similarity-based learning approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms Presents a nearest neighbor model based on a novel dissimilarity for images, and applies this for handwritten digit recognition and texture analysis Discusses a novel kernel for (visual) word histograms, as well as several kernels based on pyramid representation, and uses these for facial expression recognition and text categorization by topic Introduces an approach based on string kernels for native language identification Contains links for downloading relevant open source code With a foreword by Prof. Florentina Hristea This unique work will be of great benefit to researchers, postgraduate and advanced undergraduate students involved in machine learning, data science, text mining and computer vision. Dr. Radu Tudor Ionescu is an Assistant Professor in the Department of Computer Science at the University of Bucharest, Romania. Dr. Marius Popescu is an Associate Professor at the same institution.
650		7	_aCibernética _9138450 _2embne
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aPopescu, Marius _0Local _0http://id.loc.gov/authorities/names/n94030903 _1http://viaf.org/viaf/222458545 _998868
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-30367-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319303673
907			_a.b12949917 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11614249 _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER, GOBI_sep2018
988			_aEbook_one2one
998			_aSI _a_alco _a_vill _b10/2018 _cm _dz _ek _feng _ggw _h0
999			_c85713 _d85713 _x1