In this paper we propose a nonparametric extensionto the sparse kernel machine using a beta process prior. The extended beta process sparse kernel machine (BPSKM) allows for a sparse model to be constructed from a set of training data. The recent research on beta process reveals elegant property of Bayesian conjugate prior which is utilized to derive a variational Bayes inference algorithm. The performance of the proposed algorithm has been investigated on both synthetic and real-life data sets.
|Title of host publication||Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN)|
|Place of Publication||United States|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Number of pages||6|
|Publication status||Published - 2011|
|Event||IEEE International Joint Conference on Neural Networks: IJCNN 2011 - DoubleTree Hotel, San Jose, United States|
Duration: 31 Jul 2011 → 05 Aug 2011
https://web.archive.org/web/20110411045029/http://www.ijcnn2011.org/ (Conference website)
|Conference||IEEE International Joint Conference on Neural Networks|
|Period||31/07/11 → 05/08/11|
|Other||The International Joint Conference on Neural Networks is the premier international conference in the area of neural networks. IJCNN 2011 is organized by the International Neural Network Society (INNS), and sponsored jointly by INNS and the IEEE Computational Intelligence Society - the two leading professional organizations for researchers working in neural networks.|
IJCNN 2011 will be held at the Doubletree Hotel in San Jose, CA. It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest.