The growing availability of online text has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Some ...
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithm...
We investigate the use of self-predicting neural networks for autonomous robot learning within noisy or partially predictable environments. A benchmark experiment is performed in ...
James B. Marshall, Neil K. Makhija, Zachary D. Rot...
The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research comlnunity, and there have been a series of attempts to appl...
Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...