Probabilistic Vector Machine
نویسندگان
چکیده
Many of the classification algorithms used in practice today are based on extensions of the binary SVM classifier, which has been very successful, especially in the field of bioinformatics. SVMs run on information derived from the convex hulls of the initial training data, ignoring the data points inside the convex hull. This paper presents a new way of deriving the separating hyperplane used in linear classification which takes into account the distribution of the data and proves that this directly minimizes the probability of erroneous classification when the data satisfies a certain loose condition.
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