Semi-supervised extreme learning machine with wavelet kernel
by Nan Zhang
International Journal of Collaborative Intelligence (IJCI), Vol. 1, No. 4, 2016

Abstract: Extreme learning machine (ELM) not only is an effective classifier in supervised learning, but also can be applied on unsupervised learning and semi-supervised learning. The model structure of semi-supervised machine learning machine (SS-ELM) is same as ELM, the difference between them is the cost function. In this paper, we introduce kernel function to SS-ELM and proposed semi-supervised extreme learning machine with kernel (SS-KELM). Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and the wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Therefore, we propose semi-supervised extreme learning machine with wavelet kernel (SS-WKELM) based on the wavelet kernel function and SS-ELM. The experimental results show the feasibility and validity of SS-WKELM in classification.

Online publication date: Fri, 12-May-2017

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