Support vector regression (SVR) is well known as a or prediction tool under the Machine Learning (ML) which preserves all key features through training data. Different from general prediction, here, we proposed SVR to predict new approximate solutions after generated some iterates using an iterative method called Lanczos algorithm, one class of Krylov solvers. As know that solvers, including me...

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...

We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and ...

The Relevance Vector Machine (RVM) algorithm has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensitive applications by utilizing massively parallel accelerators such as GPUs. In this paper, the computati...

Generalized learning vector quantization (GRLVQ) is a prototype based classification algorithm with metric adaptation weighting each data dimensions according to their relevance for the classification task. We present in this paper an extension for functional data, which are usually very high dimensional. This approach supposes the data vectors have to be functional representations. Taking into...