Sparse kernel learning with LASSO and Bayesian inference algorithm.
Full text is not in this repository.
Official URL: http://dx.doi.org/10.1016/j.neunet.2009.07.001
This item is available in the Library Catalogue
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is concerned with learning kernels under the LASSO formula- tion via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages.
|Research Areas:||A. Middlesex University Schools and Centres > School of Science and Technology > Computer Science|
A. Middlesex University Schools and Centres > School of Science and Technology > Computer Science > Artificial Intelligence group
|Citations on ISI Web of Science:||2|
|Deposited On:||18 Jan 2011 14:12|
|Last Modified:||23 Feb 2015 16:01|
Repository staff only: item control page
Full text downloads (NB count will be zero if no full text documents are attached to the record)
Downloads per month over the past year