Hi, I have an idea for a machine learning project and I need some feedback.
In the article [0] https://arxiv.org/pdf/1711.01519.pdf A multinomial regression is used to find the optimal chunk size and prefetching distance. However, a multinonial regression is actually a classification algorithm so it can only choose values from a finite set. This allows to get a pretty good value for the output variables but it isn't the 'optimal' values. I propose using a regression algorithm instead that could output continuous values.The first one that comes to my mind is Nearest Neighbor Regression but I'm sure there are other ones that could be used. This would allow the regression to output the 'optimal' value of chunk size and prefetching distance instead of choosing the best one from a finite set of values. Then, I would like to implement this in HPX as an alternative to the multinomial regression and compare the performance of both algorithms. This will allow me to see if such a precision is really needed on chunk size and prefetching distance or if a classification algorithm is good enough. To be able to compare both algorithm, I assume I will have to use the same data that was used in [0]. What are your thought? Thank you. Gabriel _______________________________________________ hpx-users mailing list [email protected] https://mail.cct.lsu.edu/mailman/listinfo/hpx-users
