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007			cr nn 008mamaa
008			220601s2017 sz | s |||| 0|eng d
020			_a9783031018176
024	7		_a10.1007/978-3-031-01817-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2017 EB
100	1		_aScheirer, Walter J. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686978
245	1	0	_aExtreme Value Theory-Based Methods for Visual Recognition _cby Walter J. Scheirer
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XV, 115 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Computer Vision _x2153-1064
505	0		_aPreface -- Acknowledgments -- Figure Credits -- Extrema and Visual Recognition -- A Brief Introduction to Statistical Extreme Value Theory -- Post-recognition Score Analysis -- Recognition Score Normalization -- Calibration of Supervised Machine Learning Algorithms -- Summary and Future Directions -- Bibliography -- Author's Biography.
520			_aA common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9140972 _aPercepción visual
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9475879 _aVariables aleatorias
776	0	8	_iPrinted edition: _z9783031006890
776	0	8	_iPrinted edition: _z9783031029455
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01817-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI