1. A data classification apparatus including a computer, the data classification apparatus comprising: a separation surfaces set storage unit configured to store information defining a plurality of separation surfaces which separate a feature space into at least one known class region respectively corresponding to at least one known class and an unknown class region, wherein each of the at least one known class region is separated from outside region by more than one of the plurality of separation surfaces which do not intersect to each other; a classification unit configured to determine a classification of a classification target data whose inner product in the feature space is calculable by calculating to which region of the at least one known class region and the unknown class region determined by the information stored in the separation surface set storage unit the classification target data belongs; and a separation surface set calculation unit configured to calculate the plurality of separation surfaces based on: a plurality of training data respectively classified into any of the at least one known class and whose inner product in the feature space is calculable; and a classification of each of the plurality of training data, to store the information which defines the plurality of separation surfaces in the separation surface set storage unit, wherein the separation surface set calculation unit is configured to calculate the plurality of separation surfaces by setting minimization of a classification error of the plurality of training data, minimization of a complexity of the plurality of separation surfaces, and minimization of an area of each of the at least one known class region as optimization target, and wherein the optimization target is targeted to solve either one of the following optimization problems: min ⁢ 1 2 ⁢ w ′ ⁢ w + 1 N ⁢ ∑ i , j ⁢ ( ξ i j + + ξ i j - ) + v 1 ⁢ ∑ j ⁢ ( b j + - b j - ) - v 0 ⁢ ∑ j ⁢  b j + + b j -  subject ⁢ ⁢ to w ′ ⁢ ϕ ⁡ ( x i j ) - b j + ≤ ξ i j + w ′ ⁢ ϕ ⁡ ( x i j ) - b j - ≥ - ξ i j - ⁢ ⁢ b j + ≥ b j - ⁢ b 1 - ≥ 0 ⁢ ⁢ 0 ≥ b 2 + ⁢ ξ i j + ≥ 0 ⁢ ⁢ ξ i j - ≥ 0 ⁢ and min ⁢ 1 2 ⁢ w ′ ⁢ w + 1 N ⁢ ∑ i , j ⁢ ( ξ i j + + ξ i j - ) + ∑ j ⁢ v j ⁡ ( b j + - b j - ) - v 0 ⁡ ( b 0 + - b 0 - ) subject ⁢ ⁢ to w ′ ⁢ ϕ ⁡ ( x i j ) - b j + ≤ ξ i j + ⁢ ⁢ w ′ ⁢ ϕ ⁡ ( x i j ) - b j - ≥ - ξ i j - ⁢ ⁢ b j + ≥ b j - b 0 + ≥ 0 ⁢ ⁢ 0 ≥ b 0 - ⁢ ⁢ ξ i j + ≥ 0 ⁢ ⁢ ξ i j - ≥ 0 b j - ≥ b k + - ψ jk - ⁢ ⁢ b j + ≤ b k - + ψ jk + ⁢ ⁢ ψ jk - ⁢ ψ jk + = 0 b j - ≥ b 0 + - ψ j ⁢ ⁢ 0 - ⁢ ⁢ b 0 - ≤ b j + + ψ j ⁢ ⁢ 0 + ⁢ ⁢ ψ j ⁢ ⁢ 0 - ⁢ ψ j ⁢ ⁢ 0 + = 0 where i, j are an index of a training data, w is a weight, b j + , b j − is an intercept, each of ν 0 and ν 1 is a parameter for determining which of the standards is emphasized and a real value which is greater than 0, N is a predetermined integer, φ(x i j ) is an image of the data x i j which is i-th data belonging to j-th class in a feature space, ξ i j+ , ξ i j− are slack variables for representing an error, and ψ is a basis function of the feature space.