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Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets
As machine learning is applied to an increasing variety of complex problems, which are defined by high dimensional and complex data sets, the necessity for task oriented feature learning grows in importance.
On non-iterative training of a neural classifier
Recently an algorithm, was discovered, which separates points in n-dimension by planes in such a manner that no two points are left un-separated by at least one plane{[}1-3{]}.
Some Theorems for Feed Forward Neural Networks
We show for most classification problems three layers are sufficient and the network size depends on the number of clusters.
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