[WITHDRAWN] A Relevance Vector Machine Based on Gaussian Mixture Kernel
Mi Tong, Fang Liu, Quan Qi, Wangchen Qin
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.82How to use a DOI?
- Relevant vector machine; Gaussian mixture kernel; Gaussian mixture model.
- Relevance vector machine (RVM), a sparse Bayesian kernel method in machine learning, has been well-known for its sparsity and probabilistic predictions. Like other kernel methods, it use the kernel functions to map the input instances into higher dimensional space for problem simplicity. At present, the most widely used kernel function is Radial basis function (RBF). However, the RBF kernel does not consider the distribution information of the training samples which sometimes leads to a poor efficiency especially in semi-supervised learning where partially labeled examples are available. Therefore, in this paper, we propose a relevance vector machine base on Gaussian mixture kernel, which explores the distribution features of samples with a probabilistic density model, the Gaussian mixture model (GMM), and merge it into the training process. Applied to several datasets, the proposed method shows significantly better performance than the traditional RVM algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Mi Tong AU - Fang Liu AU - Quan Qi AU - Wangchen Qin PY - 2018/03 DA - 2018/03 TI - [WITHDRAWN] A Relevance Vector Machine Based on Gaussian Mixture Kernel BT - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/mecae-18.2018.82 DO - https://doi.org/10.2991/mecae-18.2018.82 ID - Tong2018/03 ER -