Hi Issam,
Why not starting by improving multilayer neural network before adding new
algorithms ?
To neural network expert, is it interesting to have layer configuration à la
Torch
https://github.com/torch/nn/blob/master/README.md ?
Best,
Arnaud
On 21 Mar 2014, at 10:18, Issam <[email protected]> wrote:
> Hi Mathieu,
>
> The regularized version is fundamentally different from the non-regularized
> version, in that it uses the derivative of the objective function for which
> it solves using least-square solution. The basic ELM version is classic; so,
> it wouldn't hurt having them both :). Further, it is not clear that the
> regularized version always perform better. And, many extentions of ELM assume
> the non-regularized version - like kernel-based ELMs and Sequential ELM.
>
> It shouldn't take two weeks, sorry. After implementing the non-regularized
> version it would take two more days at worst.
>
> Since I am removing sparse auto-encoders from the proposal, I will add two
> other variants of Extreme Learning Machines,
>
> 1) Weighted Extreme Learning Machines for Imbalanced Data
>
> 2) Kernel-Based Extreme Learning Machines, with,
> 2a) Radial Basis Function Kernel
> 2b) Polynomial kernel
>
> If there is time I will implement an Extreme Learning Machines version that
> increments hidden neurons without recalculating the whole least-square
> solution [1]. In other words, it can quickly find the best number of hidden
> neurons for a particular problem.
>
> Thanks.
>
> [1] Error minimized extreme learning machine with growth of hidden nodes and
> incremental learning
>
> On 3/21/2014 5:42 AM, Mathieu Blondel wrote:
>> Naive questions from someone who knows nothing about ELM. What's the
>> motivation for implementing both non-regularized and regularized ELM? If the
>> former tends to overfit, I would keep only the latter. And why do you need 2
>> weeks for implementing the regularized variant? Is the algorithm
>> fundamentally different from the non-regularized variant?
>>
>> Mathieu
>>
>>
>> On Fri, Mar 21, 2014 at 4:44 AM, Issam <[email protected]> wrote:
>> Hi all,
>>
>> I uploaded the proposal for Neural Networks to melange, here is the public
>> link.
>>
>>
>> Thank you.
>>
>> Regards,
>> ~Issam
>>
>>
>>
>> On 3/20/2014 10:47 AM, Jaidev Deshpande wrote:
>>>
>>>
>>>
>>> On Thu, Mar 20, 2014 at 5:54 AM, Issam <[email protected]> wrote:
>>> Hi all,
>>>
>>> I uploaded the Neural Network proposal to this link,
>>>
>>> https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014:-Extending-Neural-Networks-Module-for-Scikit-learn
>>>
>>> Please see if it is detailed enough as a promising proposal.
>>>
>>> Thank you.
>>> ~Issam
>>>
>>>
>>> On 3/19/2014 9:00 PM, Jaidev Deshpande wrote:
>>>>
>>>>
>>>>
>>>> On Sat, Mar 15, 2014 at 6:59 PM, Issam <[email protected]> wrote:
>>>> Thanks Olivier, I will upload the proposal very soon.
>>>>
>>>> While doing so, I will strengthen my proposal by implementing a basic
>>>> version of each of the proposed algorithms, which I will cite in my
>>>> proposal.
>>>>
>>>> Cheers. :)
>>>>
>>>> Hi Issam,
>>>>
>>>> What's the update on your proposal? I don't mean to rush you at all,
>>>> you're probably working hard on the proposal as we speak, this is just a
>>>> gentle bump.
>>>>
>>>> All the best.
>>>>
>>>>
>>>>
>>>> On 3/14/2014 5:38 PM, Olivier Grisel wrote:
>>>> > Issam if I am not mistaken you have not written an official proposal
>>>> > for this GSoC application.
>>>> >
>>>> > If you are still interested, there is an official template to follow
>>>> > for PSF affiliated sub-projects (such as scikit-learn):
>>>> >
>>>> > https://wiki.python.org/moin/SummerOfCode/ApplicationTemplate2014
>>>> >
>>>> > You can also have a look at Manoj's submission:
>>>> >
>>>> > https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014-Application:-Improved-Linear-Models
>>>> >
>>>>
>>>>
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>>>>
>>>> --
>>>> JD
>>>>
>>>>
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>>>
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>>> applications. Written by three acclaimed leaders in the field,
>>> this first edition is now available. Download your free book today!
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>>> Hi Issam,
>>>
>>> Looks OK at first glance, but please add it as a gist. Those are much
>>> easier to comment on.
>>>
>>> Thanks
>>>
>>> --
>>> JD
>>>
>>>
>>> ------------------------------------------------------------------------------
>>> Learn Graph Databases - Download FREE O'Reilly Book
>>> "Graph Databases" is the definitive new guide to graph databases and their
>>> applications. Written by three acclaimed leaders in the field,
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>>
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>> applications. Written by three acclaimed leaders in the field,
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