1. An SV reduction method for multi-class SVMs comprising: a first step of selecting, from a plurality of initially present support vectors, support vector pairs z i , z j (i, j=1, 2,. .. , N s ) which are the shortest distance between them; a second step of preparing a single-variable objective function with a single global maximum and determining a maximum value k of the objective function; a third step of applying the maximum value k to the support vector pairs z i , z j to determine a temporary vector Ztemp[i] with small classification errors; and a fourth step of representing the support vector pair z i , z j by the temporary vector Ztemp[i], wherein in a case where the objective function is optimized for a Gaussian kernel, a process of determining the maximum value k of a function h(k) of the following Formula (6) is performed in the second step, and the temporary vector Ztemp[i] is determined using the following Formula (5a) in the third step: [ Formula ⁢ ⁢ 6 ] κ = arg ⁢ ⁢ max k ⁢ h ⁡ ( k ) = ∑ t = 1 T ⁢ ( m t ⁢ K ij ( 1 - k ) 2 + ( 1 - m t ) ⁢ K ij k 2 ) m t = β ti β ti + β tj } ( 6 ) [ Formula ⁢ ⁢ 5 ⁢ a ] Ztemp ⁡ [ i ] = κ ⁢ ⁢ z i + ( 1 - κ ) ⁢ z j ( 5 ⁢ a ) wherein h(k) is an objective function optimized for the Gaussian kernel, mt is a ratio of coefficient βti of support vector, βti and βtj are coefficients of support vector, Kij is a kernel function, T is the largest value of time t, and Ns is the largest value of suffix i and j.