|Machine Learning in Optimization ? firstname.lastname@example.org (Stephan Ceram) (2008-06-02)|
|Re: Machine Learning in Optimization ? email@example.com (=?ISO-8859-1?Q?Bj=F6rn_Franke?=) (2008-06-04)|
|Re: Machine Learning in Optimization ? firstname.lastname@example.org (Stephan Ceram) (2008-06-09)|
|Re: Machine Learning in Optimization ? email@example.com (=?ISO-8859-1?Q?Bj=F6rn_Franke?=) (2008-06-10)|
|From:||Stephan Ceram <firstname.lastname@example.org>|
|Date:||9 Jun 2008 21:59:38 GMT|
|Posted-Date:||09 Jun 2008 19:07:39 EDT|
thank you for your answer.
> You may want to look at GCC ICI  (= Interactive Compilation
> Interface). Basically, GCC ICI is an initiative to open up the
> internal heuristics of GCC and allow an external tool to make the
> decisions for GCC whenever it has to decide on whether or not to apply
> a transformation or to choose a transformation parameter. So, GCC ICI
> is a GCC framework with handles to an external decision making tool
> which may or may not be based on machine learning.
I will have a look at the software.
> In terms of machine learning packages WEKA  may be useful to
> you. As WEKA is written in Java you may need to develop your own C++
> wrappers, though. Some information on bridging WEKA and .NET can be
> found in .
I'm still interested in 2 other issue:
1) how many lines of code (or benchmarks) were required in your
paper to achieve good results for machine learning, i.e. how much
code had to be passed in the training phase before the learned
data base provided reliable results for the classification of
2) The selection of static features that describe a particular C
construct (as is used in your paper to learn "good" optimizations for
different classes of programs) seems to be a crucial part. Can you
give me some advices how such "good" features for learning can
be found? Is it promising to specify as many static features of
the C programs in the training phase as possible (intuitively more
features should allow one to describe a program more precisely) or
might a too large set of features have a negative impact of machine
learning aiming at pattern recognition?
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