多尺度核方法的自适应序列学习及应用

Adaptive Sequence Learning and Applications for Multi-Scale Kernel Method

多尺度核方法是当前核机器学习领域的一个热点.通常多尺度核的学习在多核处理时存在诸如多核平均组合、迭代学习时间长、经验选择合成系数等弊端.文中基于核目标度量规则,提出一种多尺度核方法的自适应序列学习算法,实现多核加权系数的自动快速求取.实验表明,该方法在回归精度、分类正确率方面比单核支持向量机方法结果更优,函数拟合与分类稳定性更强,证明该算法具有普遍适用性.

Multi-scale kernel method is a hotspot of current kernel machine learning field. However, in the multiple kernel processing progress of multi-scale kernel learning methods, there are some disadvantages, such as average combination of kernels, time consumption increasing under iterative training and empirical selection of composite coefficients. Based on the kernel target alignment heuristics, an adaptive sequence learning algorithm for multi-scale kernel method is presented and the weighting coefficients of multiple kernels can be obtained automatically and rapidly. The experimental results testify that the proposed algorithm has better performance and stability in regression precision and classification accuracy than the SVM methods using different single kernels. Moreover, the proposed algorithm has good universal applicability.