Abstract. This paper investigates a new extension of the Probabilistic Latent Semantic Analysis (PLSA) model [6] for text classification where the training set is partially labeled...
Abstract. As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks,...
Selective sampling, a form of active learning, reduces the cost of labeling training data by asking only for the labels of the most informative unlabeled examples. We introduce a ...
: Probability distribution mapping function, which maps multivariate data distribution to the function of one variable, is introduced. Distributionmapping exponent (DME) is somethi...
We present a probabilistic approach to shape matching which is invariant to rotation, translation and scaling. Shapes are represented by unlabeled point sets, so discontinuous bou...