Hi, I've looked through the methods in benchmark system and mlpack, here is a list of some not benchmarked methods:
1. adaBoost 2. ann 3. dbscan 4. decision tree(some pr have been made) 5. gmm 6. hoeffding tree 7. mean shift clustering 8. svd 9. softmax regression As mentioned in GSoC idea list, one choice is to benchmark some of these methods against other implementation. I think it's not hard for me, the main work is reading API of mlpack and other libraries. Another idea is to speed up some method in mlpack, this one is much more difficult, and time consuming. Even though this idea is appealing to me, I don't have much confidence on the target of "the fastest of all the implementations". I think how much can we improve the speed and how to do it can only reveal after I have done enough research and experiment on one method. I plan to take the benchmarking script as the base of my proposal, and if some method is slower, try to do some analysis. If I find something, I may start to do the speed up task on one method. There is no executable of ann now, so I should write a executable for this task and do benchmarking on it? Or is it the time to provide a executable for ann? (seems ann is in developing now, if it's needed to write a wrapper of whole ann or a specific type of ann, I'm glad to do this work) Sincerely, Thyrix Yang
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