Abstract
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale problems. Experiments on synthetic and benchmark data sets confirm the feasibility and validity of the proposed algorithm. © Springer-Verlag 2004.
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CITATION STYLE
APA
Bo, L., Wang, L., & Jiao, L. (2004). Sparse bayesian learning based on an efficient subset selection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3173, 264–269. https://doi.org/10.1007/978-3-540-28647-9_45
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