A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
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In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for the kernels. However, the multi-class problems are rarely incorporated in the formulation and the optimization is time-consuming. We formulate the multi-class MKL in a bilinear form and propose a scheme for computationally efficient optimization. The scheme makes the method favorably applicable to large-scaled samples in the real-world problems. In the experiments on multi-class classification using several datasets, the proposed method exhibits the favorable performance and low computation time compared to the previous methods.