Hi Allan,
that was maybe me, it's great someone is working on the MNIST here!
1/ I'm not 100% clear about the Classifier, but I think it's just a helper
utility, unrelated to the HTM/CLA, so you've been testing performance of
any algorithm the CLassifier implements (not CLA imho). So you'd want to
create a CLA (with SP only) and place Classifier atop of it. The pipeline
would look like: {MNIST-data[ith-example]} >>> CLA(without TP) >>>(you get
SDR) >>> Classifier (add MNIST-label[ith-example]
2/ I assume the mnist dataset is created from 2D images of hadwritten
digits -> and just simply put in 1D array (??)
Then you'll lose lot of topological info passing it to the CLA just as is.
I think this will require ressurection of the Image Encoders that take into
account distance for neighborhood pixels (each pixel has 8 neighboring px),
this is used in inhibition etc.
3/ You're probably overfitting, rather experiment with 80%/20% data split.
Cheers, Mark
On Wed, Jan 22, 2014 at 5:57 PM, Allan Inocêncio de Souza Costa <
[email protected]> wrote:
>
> Hi,
>
> I read a question that someone else asked here, but I couldn't find the
> question nor the answers (if any), so I will ask again, as I'm now working
> around with the classifier.
>
> I tried to apply the classifier to the task of handwritten recognition
> using the MNIST dataset. The best result I got was an overall accuracy of
> about 42% (by that I mean that after training the entire dataset, the
> proportion of right predictions from the first to the last training example
> was 42%), after playing a little with the encoders. Of course this is
> better than the expected 10% accuracy of a random picker algorithm, but it
> falls short of what is accomplished by other (linear) algorithms. For those
> interested, I attached a plot of the accuracy.
>
> So here comes the question: what are the inner workings of the classifier?
> I'm puzzled as it doesn't have a SP. Can someone help or point to some
> reading?
>
> Best regards,
> Allan
>
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>
--
Marek Otahal :o)
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