When text categorization is applied to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. In this paper, we...
Abstract. Text Categorization algorithms have a large number of parameters that determine their behaviour, whose effect is not easily predicted objectively or intuitively and may v...
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
A new approach to the Text Categorization problem is here presented. It is called Gaussian Weighting and it is a supervised learning algorithm that, during the training phase, est...
Abstract. In this paper, we propose a probabilistic approach to feature selection for multi-class text categorization. Specifically, we regard document class and occurrence of eac...
Ke Wu, Bao-Liang Lu, Masao Uchiyama, Hitoshi Isaha...