To classify a data by reduced memories in a short calculation time.
An SVM classification device 34 classifies the propriety as a face image, as to a pick-up image of a feature vector generated as a test data by a feature vector extracting part 32, using an SVM classification expression f(x) shown in Fig.11 obtained by learning, based on a quadratic function k(x, z) shown in Fig.11 approximated with a kernel function expressed by an exponential function, and based on a plurality of feature vectors prepared preliminarily as a training data, where x and z represent the feature vectors, represents a parameter in the kernel function, a, c and q represents coefficients determined by the approximation of the kernel function, xj represents a j-dimensional feature amount of the feature vector x, xk represents a k-dimensional feature amount of the feature vector x, vi represents a support vector, vij represents a j-dimensional feature amount of the support vector vi, vik represents a k-dimensional feature amount of the support vector vi, yi represents a label of the support vector vi, n is the number of the support vectors, d represents the dimension number of the feature vectors, and 1 an b represent coefficients determined by the learning.
NAITO TAKASHI
NINOMIYA YOSHIKI
Kato Kazunori
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