  Artificial neural networks are powerful pattern classifiers; however, they
have been surpassed in accuracy by methods such as support vector machines and
random forests that are also easier to use and faster to train.
Backpropagation, which is used to train artificial neural networks, suffers
from the herd effect problem which leads to long training times and limit
classification accuracy. We use the disjunctive normal form and approximate the
boolean conjunction operations with products to construct a novel network
architecture. The proposed model can be trained by minimizing an error function
and it allows an effective and intuitive initialization which solves the
herd-effect problem associated with backpropagation. This leads to state-of-the
art classification accuracy and fast training times. In addition, our model can
be jointly optimized with convolutional features in an unified structure
leading to state-of-the-art results on computer vision problems with fast
convergence rates. A GPU implementation of LDNN with optional convolutional
features is also available
