Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differential Search Algorithm-based Parametric Optimization of Fuzzy
  Generalized Eigenvalue Proximal Support Vector Machine   Support Vector Machine (SVM) is an effective model for many classification
problems. However, SVM needs the solution of a quadratic program which require
specialized code. In addition, SVM has many parameters, which affects the
performance of SVM classifier. Recently, the Generalized Eigenvalue Proximal
SVM (GEPSVM) has been presented to solve the SVM complexity. In real world
applications data may affected by error or noise, working with this data is a
challenging problem. In this paper, an approach has been proposed to overcome
this problem. This method is called DSA-GEPSVM. The main improvements are
carried out based on the following: 1) a novel fuzzy values in the linear case.
2) A new Kernel function in the nonlinear case. 3) Differential Search
Algorithm (DSA) is reformulated to find near optimal values of the GEPSVM
parameters and its kernel parameters. The experimental results show that the
proposed approach is able to find the suitable parameter values, and has higher
classification accuracy compared with some other algorithms.

19
The categories of this paper are: eess
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex