This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from spor...

ÐWe develop an automatic text categorization approach and investigate its application to text retrieval. The categorization approach is derived from a combination of a learning paradigm known as instance-based learning and an advanced document retrieval technique known as retrieval feedback. We demonstrate the effectiveness of our categorization approach using two realworld document collections...

Categorization of text plays an important role in the text mining field. Text categorization is the process in which documents are categorized into its predefined category. Automatic text categorization is an important task due to large amount of electronic documents. This paper presents a survey of Text categorization of Indian and non-Indian languages. There is very less work done in text cat...

Formal models of categorization are psychological theories that try to describe the process of categorization in a lawful way, using the language of mathematics. Their mathematical formulation makes it possible for the models to generate precise, quantitative predictions. SUSTAIN (Love, Medin & Gureckis, 2004) is a powerful formal model of categorization that has been used to model a range of h...

Abstract We propose and axiomatize the categorical thinking model (CTM) in which framing of decision problem affects how agents categorize alternatives, that turn their evaluation it. Prominent models salience, status quo bias, loss-aversion, inequality aversion, present bias all fit under umbrella CTM. This suggests categorization is an underlying mechanism key departures from neoclassical cho...

The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracy-based analyses, as well as quantitative model-based analyses were performed. Unlike accuracy-based analyses, the mode...