Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Multi-domain learning (MDL) refers to learning a set of models simultaneously, where each
model is specialized to perform a task in a particular domain. Generally, a high labeling …

Traditional multi-class classification models are based on labeled data and are not
applicable to unlabeled data. To overcome this limitation, this paper presents a multi-class …

Analyzing arguments in user-generated Web discourse has recently gained attention in
argumentation mining, an evolving field of NLP. Current approaches, which employ fully …

This paper presents an unsupervised learning approach for simultaneous sample and
feature selection, which is in contrast to existing works which mainly tackle these two …

Sentimental analysis is the method of finding sentiment such as positive or negative from a
text data. In this paper we are using some feature selection techniques such as Mutual …

Sentiment Analysis is a sub area of Natural Language Processing (NLP) which extracts
user's opinion and classifies it according to its polarity. This task has many applications but it …

The recent years have witnessed the emerging of vector quantization (VQ) techniques for
efficient similarity search. VQ partitions the feature space into a set of codewords and …

Recently, active learning has been applied to recommendation to deal with data sparsity on
a single domain. In this paper, we propose an active learning strategy for recommendation …

Sentiment classification is a hot research topic in both industrial and academic fields. The
mainstream sentiment classification methods are based on machine learning and treat …