autoencoder: An implementation of sparse autoencoder for automatic learning of representative features from unlabeled data

The package implements the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

Version: 1.0
Published: 2014-05-21
Author: Eugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Maintainer: Yuriy Tyshetskiy <y.tyshetskiy at usask.ca>
License: GPL-2
NeedsCompilation: no
CRAN checks: autoencoder results

Downloads:

Reference manual: autoencoder.pdf
Package source: autoencoder_1.0.tar.gz
Windows binaries: r-devel: autoencoder_1.0.zip, r-release: autoencoder_1.0.zip, r-oldrel: autoencoder_1.0.zip
OS X Snow Leopard binaries: r-release: autoencoder_1.0.tgz, r-oldrel: autoencoder_1.0.tgz
OS X Mavericks binaries: r-release: autoencoder_1.0.tgz