CUED Publications database

Multiclass relevance vector machines: Sparsity and accuracy

Psorakis, I and Damoulas, T and Girolami, MA (2010) Multiclass relevance vector machines: Sparsity and accuracy. IEEE Transactions on Neural Networks, 21. pp. 1588-1598. ISSN 1045-9227

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In this paper, we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multiclass multi-kernel relevance vector machines (mRVMs) that have been recently proposed. We provide an insight into the behavior of the mRVM models by performing a wide experimentation on a large range of real-world datasets. Furthermore, we monitor various model fitting characteristics that identify the predictive nature of the proposed methods and compare against existing classification techniques. By introducing novel convergence measures, sample selection strategies and model improvements, it is demonstrated that mRVMs can produce state-of-the-art results on multiclass discrimination problems. In addition, this is achieved by utilizing only a very small fraction of the available observation data. © 2010 IEEE.

Item Type: Article
Uncontrolled Keywords: Algorithms Artificial Intelligence Bayes Theorem Classification Computational Biology Databases, Factual
Depositing User: Cron Job
Date Deposited: 12 Mar 2019 01:08
Last Modified: 15 Apr 2021 05:33
DOI: 10.1109/TNN.2010.2064787