مشاهده مشخصات مقاله
Information Theoretic Text Classification
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نویسنده (ها) |
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A. Aavani
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A. Farjudian
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M. Salmani-Jelodar
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A. Andalib
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مربوط به کنفرانس |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
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. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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خرید مقاله
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