Online updating regularized kernel

28-Jun-2017 04:59

By combining with sparse kernel methods, least-squares temporal difference (LSTD) algorithms can construct the feature dictionary automatically and obtain a better generalization ability.

However, the previous kernel-based LSTD algorithms do not consider regularization and their sparsification processes are batch or offline, which hinder their widespread applications in online learning problems.

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The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC).Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding.However, it is often difficult to choose the most suitable kernel.Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC).Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding.However, it is often difficult to choose the most suitable kernel.Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.To reduce computational complexity, Bradtke and Barto proposed a recursive LSTD (RLSTD) algorithm [1], and Xu et al. But these two algorithms still require many features especially for highly nonlinear RL problems, since the RLS approximator assumes a linear model [4].