Chinese Text Categorization study based on feature weight learning

Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different function of every feature by using feature weight learning. In this paper text categorization via K-nearest neighbor algorithm based on feature weight learning is described. The numerical experiments prove the validity of this learning algorithm.