In this paper, we propose a novel measure, called spectral measure, with sound theoretical foundation and high com- putational efficiency. The spectral measure ...
In this paper, we propose a novel kernel selection criterion based on a newly defined spectral measure of a kernel matrix, with sound theoretical foundation and ...
In this paper, we propose a novel kernel selection criterion based on a newly defined spectral measure of a kernel matrix, with sound theoretical foundation and ...
Efficient Kernel Selection via Spectral Analysis. Jian Li. 1. ;. 2. , Yong Liu. 1 ... lenging problem in kernel methods. The standard technique for kernel ...
In this paper, we propose a new algorithm for kernel discriminant analysis, called Spectral Regression. Kernel Discriminant Analysis (SRKDA). By using spectral ...
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Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang: Efficient Kernel Selection via Spectral Analysis. IJCAI 2017: 2124-2130. manage site settings.
Jan 7, 2024 · Eigenvalue decays of kernel matrices come up in most statistical and algorithmic analyses associated with kernel methods.
Abstract. We propose a novel method for addressing the model selection prob- lem in the context of kernel methods. In contrast to existing methods which ...
Our estimators not only outperform the empirical estimator, but are also simple to implement and computationally efficient. The paper is organized as follows.
Kernel selection aims at choosing an appropriate kernel func- tion for kernel-based learning algorithms to avoid either un- derfitting or overfitting of the ...