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Introduction : Classification algorithms of any kind have traditionally worked on reducing the object in question to a small set of meaningful features, however, in many cases this is not quite feasible.
Face detection, for example involves “concepts” (such as face) that cannot be reduced to manageable, quantifiable set of features, whose basis or eigen -features can be found.
Since it is not known apriori, what the relevant features for the given concept are, the feature vectors are typically large (such as the grey values of each pixel in the image).
Under such circumstances, the approach taken is to “learn” the solution from a large set of examples.
We look into a Neural Network based technique (Henry Rowley et al.) and a support-vector-machine based techniques
(Osuna et al.) which take in the large feature vector and attempt to classify the same.
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