On 2/26/2014 3:29 PM, Lars Buitinck wrote: > We have a PR that implements them, but in too convoluted a way. My > personal choice for implementing these would be a transformer doing a > random projection + nonlinear activation. That way, you can stack any > linear model on top (think SGDClassifier for large-scale work) and get > a basic ELM. I've toyed with this variant before (typing this from > memory): Yes, I am aware of the PR; it is too complex like you said; the algorithm can be developed in a much simpler way, however :).
> Part of the work for 'deep learning' in scikit-learn is > documentation and example to exihibit these patterns better. Indeed, neural networks deals a lot with stacking up kernels and classifiers on top of one another, it would be prudent to devote one complete section in Neural Networks for this. Or perhaps special pipelines to simplify such common tasks. > Does sequential mean for sequence data? Yes, by sequential I mean, training on the dataset in small batches, equivalent to "partial_fit". It would allow ELMs to work on datasets of over million row matrcies. > I think that you should open a wiki page or an issue, > or some document where we can keep track of this info and work on > building a full proposal. That's a really good idea; I will create a wiki page describing the proposal in much more details, before submitting it :). Thank you ~I ------------------------------------------------------------------------------ Flow-based real-time traffic analytics software. Cisco certified tool. Monitor traffic, SLAs, QoS, Medianet, WAAS etc. with NetFlow Analyzer Customize your own dashboards, set traffic alerts and generate reports. Network behavioral analysis & security monitoring. All-in-one tool. http://pubads.g.doubleclick.net/gampad/clk?id=126839071&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
