A kernel-free quadratic surface support vector machine for semi-supervised learning
提出一种无核半监督二次曲面支持向量机模型，通过半定规划求解，在人工和公开数据集上比高斯核方法分类更准。
In this paper, we propose a kernel-free semi-supervised quadratic surface support vector machine model for binary classification. The model is formulated as a mixed-integer programming problem, which is equivalent to a non-convex optimization problem with absolute-value constraints. Using the relaxation techniques, we derive a semi-definite programming problem for semi-supervised learning. By solving this problem, the proposed model is tested on some artificial and public benchmark data sets. Preliminary computational results indicate that the proposed method outperforms some existing well-known methods for solving semi-supervised support vector machine with a Gaussian kernel in terms of classification accuracy.