The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions in terms of a linear combination of kernel functions centred on a subset of the training data, known as support vectors. Despite its widespread success, the SVM suffers from some important limitations, one of the most...

Relevance vector machine (RVM) is a popular sparse Bayesian learning model typically used for prediction. Recently it has been shown that improper priors assumed on multiple penalty parameters in RVM may lead to an posterior. Currently the literature, sufficient conditions posterior propriety of do not allow over parameters. In this article, we propose single relevance (SPRVM) which are replace...

This paper presents a learning based approach to tracking articulated human body motion from a single camera. In order to address the problem of pose ambiguity, a one-to-many mapping from image features to state space is learned using a set of relevance vector machines, extended to handle multivariate outputs. The image features are Hausdorff matching scores obtained by matching different shape...

Harris Drucker [email protected] AT&T Research and Monmouth University, West Long Branch, NJ 07764, USA Behzad Shahrary [email protected] David C. Gibbon [email protected] AT&T Research, 200 Laurel Ave., Middletown, NJ, 07748, USA. Correspondence should be addressed to: Dr. Harris Drucker Monmouth University West Long Branch, NJ 07764 phone: 732-571-3698 email: [email protected] ...

Function approximation methods, such as neural networks, radial basis functions, and support vector machines, have been used in reinforcement learning to deal with large state spaces. However, they can become unstable with changes in the samples state distributions and require many samples for good estimations of value functions. Recently, Bayesian approaches to reinforcement learning have show...

background: prediction of interaction sites within the membrane protein complexes using the sequence data is of a great importance, because it would find applications in modification of molecules transport through membrane, signaling pathways and drug targets of many diseases. nevertheless, it has gained little attention from the protein structural bioinformatics community. methods: in this stu...