Abstract
This paper is contributed to object recognition with linear Support Vector Machine (SVM), from which we can learn the filters for recognizing the particular object categories. We compare two different SVM solvers for the primal and dual problems, and present a new fast training method, called as Partial Gradient Descent (PGD), to estimate the filters from the large-scale dataset. Our learning approach is directly proposed for the primal problem not for the dual, but it has great generalization performance and training efficiency than the popular dual optimization methods when we just want to find an approximate solution. Experiment proves the success of our learning method, especially in the large-scale truth dataset.
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