Search Results for author:
Found 2 papers, 0 papers with code
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity.
Ranked #23 on
Image Classification
on MNIST
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.
