Multiple kernel multivariate performance learning using cutting plane algorithm
This work addresses the challenge of kernel selection and parameter tuning in pattern classification, offering an incremental improvement for researchers in machine learning.
The paper tackles the problem of optimizing nonlinear, nonsmooth multivariate classifier performance measures by proposing a multi-kernel learning algorithm that unifies classifier parameter and kernel weight optimization using a cutting plane method. The results show that the algorithm outperforms competing methods on various classification tasks.
In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm by using cutting plane algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.