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Give an overview of state-of-the art classification techniques with supervised machine learning, e.g. SVM, Navie Bayes, neural network, random forests, decision trees, etc. and summarizes their pros and cons with a table

1. the literature review should give an overview of state-of-the art classification techniques with supervised machine learning, e.g. SVM, Navie Bayes, neural network, random forests, decision trees, etc. and summarizes their pros and cons with a table

2. the literature review shall be able to answer the following questions:

questions set a: what are the major steps for classification with supervised machine learning in general? What’s the gap between traditional use case of classification (specify the traditional use case) and text message classification? And therefore, what additional steps are required for text message (e.g. twitter messages or SMS) classification?

questions set b: which kind of techniques are good in general, which kind of techniques are of better performance for mutli-class classification techniques? Which kind of techniques are suit better with text message classification? …. When writing the overview of each techniques, these questions should be bear in mind, but summarized explicitly in the summarize table as pros and cons.

c. Please derive a criteria dimension for summarize pros and cons, avoid writing too much text in a table.

Note:

a. a text repetition of the summary table with pros and cons are not necessary but the pros and cons should be concise in the table, and well supported with solid evidence, with high quality reference.

b. page distribution:

2 pages overview and answer question set a

5 pages state-of-the-art methods with question set b in mind, for each method, there should be a sentence or two to describe the basic idea of this methods, then describe how it works briefly, and then the analysis (e.g. typical use case, obvious pros and cons, or some special things)

3 pages for comparison of different methods (pros and cons table)

c. no need for pictures, but it is ok to have a flow chart to compare the steps of classification with and w/o text message analysis. but it should be either a single chart with additional steps highlighted or two charts (left and right).

d. If you put some equations in the thesis, please make sure the meaning of symbols are consistent, don’t just copy, paste the original one from literature.

e. Each argument, such as “this SVM approach is widely used in XX domain, is very good” or sth similar, should be support by literature as evidence

f. use high quality literature, i.e. widely cited or written by well know researchers in the field. Use page number when cite a paper, don’t simply refer to a whole book or an entire paper with out mentioning the page number.


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