We consider the problem of online learning in a changing environment under sparse user feedback. Specifically, we address the classification of music types according to a user...
In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...
Abstract. We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate ...
In this paper, we study the use of heterogeneous data for training of acoustic models. In initial experiments, a significant drop of accuracy has been observed on in-domain test s...
We investigate the effectiveness of selftraining PCFG grammars with latent annotations (PCFG-LA) for parsing languages with different amounts of labeled training data. Compared to...