I assumed you intended to have additional unlabelled data, but perhaps
working out the best way to incorporate the unlabelled data into the
fitting procedure (particularly if you support partial_fit) might be a big
question of its own. So I'm +1 for delaying that decision :)


On 27 May 2014 19:43, Olivier Grisel <notifications@github.com> wrote:

>  In benchmarks/bench_mnist.py:
>
> > +=======================
> > +
> > +Benchmark multi-layer perceptron, Extra-Trees, linear svm
> > +with kernel approximation of RBFSampler and Nystroem
> > +on the MNIST dataset.  The dataset comprises 70,000 samples
> > +and 784 features. Here, we consider the task of predicting
> > +10 classes -  digits from 0 to 9. The experiment was run in
> > +a computer with a Desktop Intel Core i7, 3.6 GHZ CPU,
> > +operating the Windows 7 64-bit version.
> > +
> > +    Classification performance:
> > +    ===========================
> > +    Classifier            train-time  test-time error-rate
> > +    ------------------------------------------------------
> > +    nystroem_approx_svm     124.819s    0.811s    0.0242
> > +    MultilayerPerceptron    359.460s    0.217s    0.0271
>
> Isn't it possible to find hyperparams values to reach better accuracy with
> tanh activations? It should be possible to go below 2% error rate with a
> vanilla MLP on mnist.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/3204/files#r13069391>
> .
>