مشاهده‌ مشخصات مقاله

Information Theoretic Text Classification

Authors	A. Aavani A. Farjudian M. Salmani-Jelodar A. Andalib
Conference	دوازدهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران
Abstract	The assignment of natural language texts to two or more predefined categories based on their contents, is an important component in many information organization and management tasks. This paper presents an information theoretic approach for text classification problem that we call it ITTC. Here, we prove that ITTC is theoretically equivalent to Bayesian classifier. However, when classification task is performed over dynamic or noisy data, or when the training data do not represent all probable cases, ITTC outperforms Bayesian classifier. We also show that the complexity of ITTC over test set grows linearly by the size of input data .We use some news groups, to evaluate the superior performance of our approach.
قیمت	برای اعضای سایت : 100,000 Rial برای دانشجویان عضو انجمن : 20,000 Rial برای اعضای عادی انجمن : 40,000 Rial

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