We argue that learning problems in the visual domain have sparseness characteristics and exhibit them by analyzing data taken from face detection experiments.

Abstract. Numerous statistical learning methods have been developed for visual recognition tasks. Few attempts, however, have been made.

SNoW and SVM, presented in Section 2, are representatives of two different classes of linear classifiers; the first is based on Winnow, a multiplicative update ...

In this paper, we present a theoretical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational ...

It is argued that learning problems in the visual domain have sparseness characteristics and exhibit them by analyzing data taken from face detection ...

In this paper, we present a theo- retical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational ...

In this paper, we present a theoretical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational ...

Large margin classifiers, such as SNoW and SVM, have recently demonstrated their success in object detection and recognition. In this paper, we present a ...

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A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition. Conference ... and inversely, two very diverse classifiers will necessarily have poor ...