[mlpack] contribute to RBFN
Marcus Edel
marcus.edel at fu-berlin.de
Mon Mar 2 13:23:40 EST 2015
Hello Shangtong,
Sorry for the slow response. Great to see that you are interested in
implementing Radial basis function networks.
> […] I think it’s better to model the whole RBFN as one layer class in current framework of ANN.
> Then this RBFN layer can be used to build other network such as CNN, FFNN.
> Is it appropriate? […]
Sounds like a great idea, in fact my plan is to use the network for some
reinforcement learning.
Thanks,
Marcus
P.S.: I need some more time to go through the rest of the convolution neural
network code you're coding output and adjustment is way faster than my ability
to check the code.
> On 02 Mar 2015, at 07:35, Shangtong Zhang <zhangshangtong.cpp at qq.com> wrote:
>
> Hi,
>
> I want to contribute to RBFN.
> Here is my understanding about implementing RBFN in current framework.
> According to wikipedia, http://en.wikipedia.org/wiki/Radial_basis_function_network <http://en.wikipedia.org/wiki/Radial_basis_function_network>,
> RBFN has three layers,
> but I think it’s better to model the whole RBFN as one layer class in current framework of ANN.
> Then this RBFN layer can be used to build other network such as CNN, FFNN.
> Is it appropriate?
>
> Best
> Regards
>
>
> Shangtong Zhang,
> Third Year Undergraduate,
> School of Computer Science,
> Fudan University, China.
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