Editing Authorship detection: 2010 group (section)

====Jie Dong====
'''Progress and Status this week:'''
# Hidden Markov model is implemented using Java, and the program produces a table containing probabilities information for some common trigrams from some texts input. The problem with it currently is because I am feeding all words appeared in texts into the program, there are few common trigrams among certain number of input texts. For example, I have tried with total 20 input texts from two authors, the number of trigram they do have in common is just one. In this case, I also set the program to allow part of these texts to have common trigram and others just put zero probabilities for these trigrams, the result is still not efficient.
# Read through Tails trigram description and code, I found that he simplified the method and extracted the key specification by deleting the non key words. By testing his idea using Java code, I found it does extract a lot more information than mine, however a question also raised to me is that whether it would reduce the accuracy of classification since it changes  original text to another. This simplification needs to be proved.
# Produced result by extraction algorithm is fed into MATLAB SVM methods (svmtrain and svmclassify),it shows my extraction algorithm is not working properly. Sometimes, the predicted author for chosen texts are correct and sometimes are not. In term of SVM itself, it only supports classifying for two groups and multi-group classification produces error. In addition, they can only plot SVM structure for two dimensional data. Hence, more enhanced SVM toolboxes should be studied.

'''Plan and Goals for next week:'''
# GUI design
# Test efficiency using different groups of input texts
# Try another SVM toolbox from: http://asi.insa-rouen.fr/enseignants/~arakotom/toolbox/index.html