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Support Vector Machines (SVMs) is a popular classification and regression prediction tool that uses supervised machine learning theory to maximize the predictive accuracy. This paper focuses on the field programmable gate array (FPGA) implementation of a Support Vector Machine classification system. Owing to the advanced parallel calculation feature provided by FPGA, a fast data classification can...
In this paper, a novel spatial-spectral kernel method is proposed for classification in hyperspectral images. In this spatial-spectral multiple-kernel learning (S2MKL) method, extended morphological features as spatial information and originally spectral features are used as input features. Moreover, we implement a kernel EMP (KEMP) to better extract the spatial features. With those spatial and spectral...
In this paper, we propose a new sparse multiple-kernel learning (MKL) method for classification of hyperspectral images. The proposed method adopts two-step strategy to carry out model learning from multiple basis kernels rather than simultaneously optimizing the kernel combination and learning performance. In the first step, we firstly reformulate the multiple-kernel learning so as to making the...
Brain-computer interface (BCI) system uses brain activity to control external devices such as computers and electronic devices. It is a novel kind of human computer interaction. BCI system can be regard as pattern recognition system, and the key point is classification of Electroencephalogram (EEG) signals under different mental tasks. Classification algorithms of BCI system include Fisher linear...
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