Extreme Value Theory-Based Methods for Visual Recognition

Lingua: inglese

Editore: Springer International Publishing Feb 2017, 2017

3031006895 / 9783031006890

Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

Venditore con 5 stelle

Venditore AbeBooks dal 11 gennaio 2012

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Descrizione dell’articolo da parte del venditore

This item is printed on demand - it takes 3-4 days longer - Neuware -A 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. 132 pp. Englisch.…

Codice articolo 9783031006890

Titolo
Extreme Value Theory-Based Methods for Visual Recognition
Autore
Walter J. Scheirer
Editore
Springer International Publishing Feb 2017
Anno di pubblicazione
2017
Condizione
Neu
Rilegatura
Taschenbuch
Lingua
inglese
ISBN 10
3031006895
ISBN 13
9783031006890
Peso dell'articolo
262 grammi
Dimensioni
235x191x8 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 11 gennaio 2012

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 5 a 15 giorni lavorativiDa 5 a 15 giorni lavorativi
Primo articoloEUR 23,00EUR 23,00
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