Abstract
Usually, the solution of the conventional extreme learning machine, which is a type of single-hidden-layer feedforward neural networks, is not sparse.
In this paper, to overcome this problem, we discuss a sparse extreme learning machine using empirical feature mapping. Here, the basis vectors of empirical feature space are the linearly independent training vectors. Then, unlike the conventional extreme learning machine, only these linearly independent training vectors become support vectors. Hence, the solution of the proposed method is sparse. Using UCI bench-mark datasets, we evaluate the effectiveness of the proposed method over the conventional methods from the standpoints of the sparsity and classification capability.
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Kitamura, T. (2016). Sparse Extreme Learning Machine Classifier Using Empirical Feature Mapping. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_57
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DOI: https://doi.org/10.1007/978-3-319-44778-0_57
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