Psorakis Ioannis, Damoulas Theodoros, Girolami Mark A
Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK.
IEEE Trans Neural Netw. 2010 Oct;21(10):1588-98. doi: 10.1109/TNN.2010.2064787. Epub 2010 Aug 30.
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.
在本文中，我们研究了最近提出的两种近似贝叶斯分类算法——多类多核相关向量机（mRVM）的稀疏性和识别能力。通过在大量真实世界数据集上进行广泛实验，我们深入了解了mRVM模型的行为。此外，我们监测各种模型拟合特征，以确定所提出方法的预测性质，并与现有分类技术进行比较。通过引入新颖的收敛度量、样本选择策略和模型改进，结果表明mRVM在多类判别问题上能够产生领先的结果。此外，这仅通过使用可用观测数据的极小一部分就能实现。
