Nonlinear System Identification With Composite Relevance Vector Machines

Variational Relevance Vector Machines

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...

Robust Reinforcement Learning with Relevance Vector Machines

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...

Support Vector Machines for System Identification

Fast FPGA System for Training Nonlinear Support Vector Machines

Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGAbased system for nonlinear SVM training using ensemble learning is presented. The proposed fram...

Multivariate Relevance Vector Machines for Tracking

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...