Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection prob...
Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan...
Plan recognition has traditionally been developed for logically encoded application domains with a focus on logical reasoning. In this paper, we present an integrated plan-recogni...
Localization is a fundamental challenge for autonomous robotics. Although accurate and efficient techniques now exist for solving this problem, they require explicit probabilistic...
Armita Kaboli, Michael H. Bowling, Petr Musí...
Representing lexicons and sentences with the subsymbolic approach (using techniques such as Self Organizing Map (SOM) or Artificial Neural Network (ANN)) is a relatively new but i...
We present an extensible supervised Target-Word Sense Disambiguation system that leverages upon GATE (General Architecture for Text Engineering), NSP (Ngram Statistics Package) an...
Mahesh Joshi, Serguei V. S. Pakhomov, Ted Pedersen...
The scarcity of manually labeled data for supervised machine learning methods presents a significant limitation on their ability to acquire knowledge. The use of kernels in Suppor...
Mahesh Joshi, Ted Pedersen, Richard Maclin, Sergue...
We introduce the generalized semi-Markov decision process (GSMDP) as an extension of continuous-time MDPs and semi-Markov decision processes (SMDPs) for modeling stochastic decisi...
Unmotivated students do not reap the full rewards of using a computer-based intelligent tutoring system. Detection of improper behavior is thus an important component of an online...
We introduce a novel intelligent system which can generate new Chinese calligraphic artwork that meets certain aesthetic requirements automatically. In the machine learning phase,...
Songhua Xu, Francis C. M. Lau, Kwok-Wai Cheung, Yu...