1. A computer implemented method for classifying a document using a plurality of confidence grades, comprising: providing a training engine and a classification engine employing a computer system for classifying said document using said plurality of confidence grades, wherein said computer system further comprises a processor, a memory unit for storing programs and data, an input/output (I/O) controller, a network interface, and a data bus; training a classifier by said training engine using a plurality of training documents, wherein training said classifier comprises: obtaining a list of first words from said training documents, wherein each of said training documents is classified into one of a plurality of classes; for each class belonging to said classes: determining a prior probability of said class, wherein said prior probability is probability of occurrence of said training documents in said class given said training documents in said classes; calculating conditional probabilities for said list of first words; calculating a minimum posterior probability and a maximum posterior probability for said class; defining multiple threshold constants by evaluating empirically said calculated minimum and maximum posterior probabilities across a number of experimentations; determining a plurality of confidence thresholds using said list of first words, said prior probability of said class, and one of said multiple threshold constants; and defining said confidence grades using said determined confidence thresholds; classifying said document by said classification engine using said trained classifier, wherein classifying said document comprises: obtaining a list of second words from said document, wherein said second words are commonly present in said list of first words; determining conditional probabilities for said list of second words for each of said classes from said calculated conditional probabilities for said list of first words; calculating a posterior probability for each of said classes using said prior probability and said determined conditional probabilities; comparing said calculated posterior probability with said determined confidence thresholds for each of said classes; assigning each of said classes to one of said defined confidence grades based on said comparison of said calculated posterior probability with said determined confidence thresholds; and assigning said document to one of said classes based on said calculated posterior probability and said assigned confidence grades for each of said classes; whereby said document is classified into one of said classes using said confidence grades.
1. A computer implemented method for classifying a document using a plurality of confidence grades, comprising: providing a training engine and a classification engine employing a computer system for classifying said document using said plurality of confidence grades, wherein said computer system further comprises a processor, a memory unit for storing programs and data, an input/output (I/O) controller, a network interface, and a data bus; training a classifier by said training engine using a plurality of training documents, wherein training said classifier comprises: obtaining a list of first words from said training documents, wherein each of said training documents is classified into one of a plurality of classes; for each class belonging to said classes: determining a prior probability of said class, wherein said prior probability is probability of occurrence of said training documents in said class given said training documents in said classes; calculating conditional probabilities for said list of first words; calculating a minimum posterior probability and a maximum posterior probability for said class; defining multiple threshold constants by evaluating empirically said calculated minimum and maximum posterior probabilities across a number of experimentations; determining a plurality of confidence thresholds using said list of first words, said prior probability of said class, and one of said multiple threshold constants; and defining said confidence grades using said determined confidence thresholds; classifying said document by said classification engine using said trained classifier, wherein classifying said document comprises: obtaining a list of second words from said document, wherein said second words are commonly present in said list of first words; determining conditional probabilities for said list of second words for each of said classes from said calculated conditional probabilities for said list of first words; calculating a posterior probability for each of said classes using said prior probability and said determined conditional probabilities; comparing said calculated posterior probability with said determined confidence thresholds for each of said classes; assigning each of said classes to one of said defined confidence grades based on said comparison of said calculated posterior probability with said determined confidence thresholds; and assigning said document to one of said classes based on said calculated posterior probability and said assigned confidence grades for each of said classes; whereby said document is classified into one of said classes using said confidence grades. 4. The computer implemented method of claim 1 , wherein said confidence thresholds are determined for each of said classes based on average frequency of occurrence of each of said first words and average size of each of said training documents.