一种新型的自适应多核学习算法

针对样本基数较大、 维数较高、 特征较复杂的数据集训练问题, 将支持向量机与蚁群优化算法相融合, 提出一种自适应多核学习算法. 利用吸引子传播聚类算法自适应地发现相似特征, 并据此利用蚁群算法自适应地选择核函数参数, 从而快速选择最优核函数. 通过UCI数据集的5组数据实验表明, 该算法相比于传统的支持向量机分类准确率和F₁值更高, 验证了该算法的有效性和可行性.

A Novel Self-adaptive Multiple Kernel Learning Algorithm

Aiming at the problem of the training data set with large samples, high dimension, and complex features, a self-adaptive multiple kernel learning algorithm was proposed by integrating support vector machine with ant colony optimization algorithm. The affinity propagation clustering algorithm was used to find the similar features adaptively, and then the parameters of the kernel function were selected adaptively by ant colony algorithm, so as to select the optimal kernel function quikly. Experimental results of five groups of UCI data sets show that the proposed algorithm has higher classification accuracy and F₁ value than the traditional support vector machine, which verifies the effectiveness and feasibility of the proposed algorithm.