Re: DATA MINING: Use of modern heuristics to transform and select regressors for linear modelling

On Aug 13, 2:17 pm, Cimode <cim..._at_hotmail.com> wrote:
> On Aug 13, 2:08 pm, Shah <shahryar.rah..._at_gmail.com> wrote:
>
>
>
> > Hi,
> > I am working on a project that intends to investigate the
> > implementation of a modern heuristic (e.g. simulated annealing,
> > genetic algorithms or local search) to search through a space of
> > polynomial transformations and assign selections for a linear
> > regression.
>
> > I have read that standard statistical methods for finding suitable
> > transformations of regressors use hill-climbing algorithms to search
> > for the correct transformations for linear modelling. I have found
> > that alot of times techniques such as stepwise regression have been
> > used to select a subset of regressors using a greedy algorithm.
>
> > BUT when this technique is used on a more complex model these
> > algorithms would fail to reach a global optimum.
>
> > I would like to know if by adopting a heuristic technique it may be
> > possible to provide better results.
>
> > (Could anyone post any suggestions/possible reading material/anything
> > that has been done along the same lines)
>
> > Thanks,
>
> If your purpose is to support statistical interpolation of values in
> time series then I suggest you take a look at the following
>
> http://www.eyrolles.com/Informatique/Livre/9781558608559/
>
> Hope this helps...

Just a little extra note. No matter how *light* can a procedural algorhytm be, it simply can not compare to a set operation on that matter. Received on Mon Aug 13 2007 - 14:22:54 CEST