1. Seungchan Lee Department of Electrical and Computer Engineering Mississippi State University RVM Implementation Progress

2. Page 1 of 3 Research Presentation Relevance Vector Machine Why is Relevance Vector Machine?  Problems of SVM  Use large number of basis function  Binary classification, (does not consider probabilistic methods )  How can overcome this?  Adopt probabilistic Bayesian approach  Relevance vector learning becomes the optimization of hyperparameters.

3. Page 2 of 3 Research Presentation Progress Current problem  Initialization problem for large data set  Inverse matrix problem  RVM class (in progress) Progress as of (Won’s work)  Positive definite matrix problem  The error occurs on Cholesky decomposition step  In order to fix this problem, it is required to fully understand RVM algorithm.  Make simple IFC program  300 data samples in-class and out-class started to cause the positive definite problem

4. Page 3 of 3 Research Presentation Plan  Fix positive definite matrix problem in the Cholesky decomposition step  check the positive definite matrix before this step  Improving efficiency  Divide and Conquer Approach  To make a whole vector to small subset of vectors  Find alternatives – speech recognition needs larger size of vectors  Reduce memory and computation time of the Cholesky decomposition step  Constructive approach by Tipping and Faul  Sequential Bootstrapped SVM method