A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal...
Support Vector Machine (SVM) has been spotlighted in the machine learning community thanks to its theoretical soundness and practical performance. When applied to a large data set...
In this report, we present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Based on a Fourier deco...
In a recent paper Prinz et al. (Nature Neurosci. 7, 1345-52 (2004)) have addressed the fundamental question, whether neural systems are built with a fixed blueprint of tightly con...