The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms is to require that the predicted data labels should be sufficiently smooth with respect to the intrinsic data manifold. In this paper, we argue that rather than penalizing the label smoothness, we can directly punish the discriminality of the classification function t...

This paper addresses automatic classification of baboon vocalizations. We considered six classes of sounds emitted by Papio papio baboons, and report the results of supervised classification carried out with different signal representations (audio features), classifiers, combinations and settings. Results show that up to 94.1% of correct recognition of pre-segmented elementary segments of vocal...

This paper addresses the problem of supervised classification using general Bayesian learning. General Bayesian learning consists of estimating the unknown class-conditional densities from a set of labelled samples. However, the estimation requires to evaluate intractable multidimensional integrals. This paper studies an implementation of general Bayesian learning based on MCMC methods.

We examine supervised learning for multi-class, multi-label text classification. We are interested in exploring classification in a realworld setting, where the distribution of labels may change dynamically over time. First, we compare the performance of an array of binary classifiers trained on the label distribution found in the original corpus against classifiers trained on balanced data, wh...

Electronics gadgets are part of human life in these days, as a result abundant data is generated and it is growing in exponential rate. Data Generated was earlier stored in dumped repositories. The paper attempts in proposing a classified repository so that at later retrieval of stored data or navigation becomes easy. In the present paper comparison between supervised and semi-supervised classi...

Sentiment lexicons are widely used as an intuitive and inexpensive way of tackling sentiment classification, often within a simple lexicon word-counting approach or as part of a supervised model. However, it is an open question whether these approaches can compete with supervised models that use only word-representation features. We address this question in the context of domain-specific sentim...