The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed,...
Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vas...
We perform a systematic evaluation of feature selection (FS) methods for support vector machines (SVMs) using simulated high-dimensional data (up to 5000 dimensions). Several findi...
—In this paper, we study how a humanoid robot can learn affordance relations in his environment through its own interactions in an unsupervised way. Specifically, we developed a...
Baris Akgun, Nilgun Dag, Tahir Bilal, Ilkay Atil, ...