Hi Gabriel,

Sorry for my late responses, as I am not at university anymore and I am
working at Oracle so I am kind of busy here with my ongoing projects. But I
would like to help you understand the concepts as much as possible. However
you could also count on Patrick and also Dr. Kaiser if you found me not
easy to find ;)
About your question, yes you can choose different candidates values and
even different amounts of candidates. But they should be chosen wisely. You
can do tests on your chosen candidates to see how your learning model acts
using them. If random candidates are chosen, your model will not result as
You are also free to choose different learning model.
However our main focus in this project is to use online machine learning
methods. Such that it will not required to collect offline data anymore. So
the system learns as it receives new data through time. But for the first
phase of this project please continue working and studying different
learning models and may trying with different candidates.
If it’s possible and you have time, you can also start writing your
proposal, so we could finalize it together. It’s the best way to clarify
the steps of this project.


On Fri, Mar 2, 2018 at 4:46 PM Gabriel Laberge <gabriel.labe...@polymtl.ca>

> Hi,
> I ask you many question as of recently but that's because I'm really
> getting invested in the project.
>        So,the multinomial regressions used to find the optimal chunk
> size and prefetching distance is in actuality a multi-class
> classification algorithm.But, I wanted to ask you if chunk size and
> prefetching distance could be interpreted as continuous variables. For
> example, your regression can only return chunk size of 0.1% 1% 10% and
> 50%. But do chunk sizes of 34%,23% or 56% make sense? I'm not sure.
> Also for prefetching distance, the values used are 10 50 100 1000
> 5000. But would values of 543,23 or 4851 also make sense?
>        If yes, I believe those variables could be treated as 'almost'
> continuous variables so a regression algorithm could be used instead
> of a classification one. This would allow to get more precise values
> since the regression could (in the case of prefetching distance)
> output any integer between 0 and 5000. Such a regression could be
> compared to the multinomial regression model in order to find out if
> such a precision is really required.
> Thank you very much.
> Gabriel.
> --
Best Regards,

*Zahra Khatami* | Member of Technical Staff
Virtual OS
400 Oracle Parkway
Redwood City, CA 94065
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