Patent Document ID: 8626682
Application ID: 13046266

Base Claim:
1. A method of training an initially trained classifier (ITC), the ITC having been generated using a set of verified documents associated with a set of class labels, the set of verified documents having been divided into a training set of documents and a test set of documents, and each class of the set of class labels associated with a class list, the training set of documents having been further divided into an integer number of verified document sets (INVDS), the method comprising: automatically inputting a set of unverified documents into the ITC, the set of unverified documents divided into an integer number of unverified document sets (UNVDS); automatically identifying a subset of documents from the UNVDS; automatically generating a final set of training documents based on the subset of documents and the INVDS; clustering at least one subset of documents from the INVDS and one subset of documents from the UNVDS using a flat clustering or hierarchical clustering technique; and training the ITC using the final set of training documents.

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Claim 2:
2. The method of claim 1 , further comprising: executing a first loop code segment comprising a first loop construct written in a computer programming language, wherein the first loop code segment is executed at run time at least n times, wherein n is a value at run time of a first variable in a first loop termination condition; executing a second loop code segment comprising a second loop construct written in the computer programming language, wherein the second loop code segment is executed at least p×n times, wherein p is a value at run time of a second variable in a second loop termination condition; and executing a third loop code segment comprising a third loop construct written in the computer programming language, wherein the third loop code segment is executed p×n×q times, wherein q is a value at run time of a third variable in a third loop termination condition.