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008			150903s2005 ne | o |||| 0|eng d
020			_a9781402032752 _99781402032752
024	7		_a10.1007/1402032757 _2doi
035			_avtls000334291
039		9	_a201509030245 _bVLOAD _c201404120722 _dVLOAD _c201404090501 _dVLOAD _y201402041144 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aT385
100	1		_aSebe, N. _eautor _9306715
245	1	0	_aMachine Learning in Computer Vision / _cby N. Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang.
264		1	_aDordrecht : _bSpringer Netherlands, _c2005.
300			_axv, 242 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aComputational Imaging and Vision, _x1381-6446 ; _v29
500			_aSpringer eBooks
505	0		_aTheory: Probabilistic Classifiers -- Theory: Generalization Bounds -- Theory: Semi-Supervised Learning -- Algorithm: Maximum Likelihood Minimum Entropy HMM -- Algorithm: Margin Distribution Optimization -- Algorithm: Learning the Structure of Bayesian Network Classifiers -- Application: Office Activity Recognition -- Application: Multimodal Event Detection -- Application: Facial Expression Recognition -- Application: Bayesian Network Classifiers for Face Detection.
520			_aThe goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aCohen, Ira. _eautor _9306716
700	1		_aGarg, Ashutosh. _eautor _9306717
700	1		_aHuang, Thomas S. _eautor _9301775
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781402032745
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-4020-3275-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c281216 _d281216