Text classification poses some specific challenges. One such challenge is its high dimensionality where each document (data point) contains only a small subset of them. In this pap...
There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data - multi-labelity. This arises due to the fact that a ...
Mohammad Salim Ahmed, Latifur Khan, Nikunj C. Oza,...
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome t...
Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likeliho...
Joshua Dillon, Krishnakumar Balasubramanian, Guy L...