Multi-class sentiment classification has extensive application backgrounds, whereas studies
on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment …

Ensembling is a popular and powerful technique to utilize predictions from several different
machine learning models. The fundamental precondition of a well-working ensemble model …

One-hot encoding is the prevalent method used in neural networks to represent multi-class
categorical data. Its success stems from its ease of use and interpretability as a probability …

Multi-class analysis, as the term suggest is the classification of the data in more than two
classes. However not much studies were focused on such analysis and researchers often …

The analysis and classification of images, such as texture images, is one of the substantial
and important fields in image processing. Due to destructive effects of image rotation and …

Naïve Bayes (NB) classifier has shown amazing performance in many real applications.
However, the true probability distributions are usually unknown and tend to be quite …

En classification multi-labels graduée (CMLG), chaque instance est associée à un ensemble
de labels avec des degrés d'association gradués. Par exemple, une même molécule …

Models for the classification of vowels are of continuing interest in both phonetics and for the
development of automatic speech recognition (ASR) systems. The phonetics researchers …

We examine several aspects of explicability of a classification system built from neural
networks. The first aspect is the pairwise explicability, which is the ability to provide the most …

Les données provenant de différentes sources sont continuellement collectées et
entreposées grâce au développement continu des outils de stockage. La préoccupation de …