CCCP pooling layer

This pull request is to add a cascadable cross channel parametric(cccp) pooling layer.
The output feature maps of this layer is an parametric recombination of the input feature map.
It is used with relulayer on top of a convolutional layer. Each patch from the convolution input is mapped to its feature vector in the output feature map through a nonlinear function (multilayer perceptron).
The function is equivalent to 1x1 convolution. However, if convlayer is used for this purpose, there is unnecessary im2col operation.

The cccp layer is used to implement the idea in this paper: [Network In Network](http://arxiv.org/abs/1312.4400), which achieved best performance on cifar-10 and cifar-100 datasets. As is shown [here](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html).

Using the long name cascadable cross channel parametric pooling is because I want the abbrev to be the same with [Союз Советских Социалистических Республик](http://en.wikipedia.org/wiki/Soviet_Union), which is just cool.

Please don't merge yet, tests and examples will be added soon.