Adapting stimuli to stabilize neural responses is an important problem in the context of cortical prostheses. This paper describes two approaches for stimulus adaptation using supp...
Dominik Brugger, Sergejus Butovas, Martin Bogdan, ...
Abstract. We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of S...
Abstract. We report work on the mapping between the speech signal and articulatory trajectories from the MOCHA database. Contrasting previous works that used Neural Networks for th...
Electropalatography is a well established technique for recording information on the patterns of contact between the tongue and the hard palate during speech. It leads to a stream ...
—Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density...
Guangyu Zhu, Dawei Liang, Yang Liu, Qingming Huang...
— We consider the regression problem for financial time series. Typically, financial time series are non-stationary and volatile in nature. Because of its good generalization p...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...
This study develops a novel model, GA-SVR, for parameters optimization in support vector regression and implements this new model in a problem forecasting maximum electrical daily...
Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector...
Most up-to-date well-behaved topic-based summarization systems are built upon the extractive framework. They score the sentences based on the associated features by manually assig...
— To obtain accurate modeling results, it is of primal importance to find optimal values for the hyperparameters in the Support Vector Regression (SVR) model. In general, we sea...