Re: Question related to the book

Thank you, Eibe, for the prompt reply.

Cheers,
Edward

On Mon, Jun 14, 2021 at 2:07 PM Eibe Frank <[email protected]> wrote:

> Feature extraction using PrincipalComponents or MultiClassFLDA is an
> example of simple representation learning: these filters map the data into
> a new feature space (i.e., representation) that may make learning easier.
>
> More sophisticated representation learning generally involves deep
> learning. Check out the wekaDeepLearning4j package for info on how to use
> deeplearning4j in WEKA (https://deeplearning.cms.waikato.ac.nz/). You
> could also use the wekaPyScript package to apply a Python-based deep
> learning library in a WEKA filter or classifier.
>
> Cheers,
> Eibe
>
> > On 14/06/2021, at 6:00 PM, Edward Wiskers <[email protected]>
> wrote:
> >
> > Hi all,
> >
> > In the last version of the book, especially on page 418, it was
> highlighted that Representation learning techniques transform features into
> some intermediate representation prior to mapping them to final
> predictions.
> >
> > Can any of the authors explain the idea behind Representation learning
> techniques? How it differs from classical machine learning and provide an
> example algorithm related to this category in Weka?
> >
> >
> > Thanks in advance.
> > Edward
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