Abstract of ECCV 2002 paper
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
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Abstract
Numerous statistical learning methods have been developed
for visual recognition tasks.
Few attempts, however, have been made to address theoretical issues,
and in particular, study the suitability of different learning
algorithms for visual recognition.
Large margin classifiers, such as SNoW and SVM, have recently
demonstrated their success in object detection and recognition.
In this paper, we present a theoretical account of these two learning
approaches, and their suitability to visual recognition.
Using tools from computational learning theory, we show that the
main difference between the generalization bounds of
SVM and SNoW depends on the properties of the data.
We argue that learning problems in the visual domain have
sparseness characteristics and exhibit them by analyzing data taken from
face detection experiments.
Experimental results exhibit good generalization and robustness
properties of the SNoW-based method, and conform to the theoretical
analysis.
Acknowledgments
We would like to thank Ryan Rifkin and Prof. Tomaso Poggio for the use
of MIT CBCL face database in our experiments.
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