Method and apparatus for incremental computation of the accuracy of a categorization-by-example system

Abstract

Disclosed are methods and for incrementally updating the accuracy provided by documents in training set of used for automatic categorization. A k-nearest neighbor database includes the documents in the training set, categories, category assignments of the documents and category scores for the documents. A list made up of the nearest neighbors of the documents and corresponding similarity scores contains is maintained by the method. On adding or deleting documents or category assignments, the documents influenced by the changed documents or category assignments are identified. The category scores of the identified documents are updated to be consistent for the updated training set and a new precision and recall curves are computed for the categories including updated category scores. The precision and recall curves may be used to determine an optimal number of documents to maximize the return of relevant documents while minimizing the total number of documents.