TEXT CATEGORIZATION Building a kNN classifier for the Reuters-21578 collection
نویسنده
چکیده
Categorization of texts into topical categories has gained booming interest over the past few years. There is a growing need for tools that help in finding, filtering and managing the highdimensional data due to the rapid growth of online information. Building a text classifier by hand is time consuming and costly and hence automated text categorization has gained a lot of importance. A general inductive process automatically builds a classifier by learning, from a set of previously classified documents, the characteristics of one or more categories. In this project we look at the main approaches that have been taken towards text categorization. Also, the K-nearest neighbour algorithm is used for building a classifier for the Reuters collection.
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