We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, t...
Speech processing is an important aspect of affective computing. Most research in this direction has focused on classifying emotions into a small number of categories. However, nu...
Dongrui Wu, Thomas D. Parsons, Emily Mower, Shrika...
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
The main purpose of this paper is to propose an incorporating a grammatical evolution (GE) into the genetic algorithm (GA), called GEGA, and apply it to estimate the compressive s...
Hsun-Hsin Hsu, Li Chen, Chang-Huan Kou, Tai-Sheng ...