createDataSet

Input data, possibly also target data if a formula is used.

data

targets

Model <code><a rd-options="" href="/link/formula?package=darch&version=0.10.0" data-mini-rdoc="darch::formula">formula</a></code>.

formula

<code><a href="/link/?package=darch&version=0.10.0" dataset'="" data-mini-rdoc="darch::"></a>'>DataSet</code> to be used as the basis for the
new one

dataSet

Create data set using data, targets, a formula, and possibly an existing data
set.

The darch package is built on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets). This package is for generating neural networks with many layers (deep
architectures) and train them with the method introduced by the publications
"A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
Y. W. Teh; doi: 10.1162/neco.2006.18.7.1527) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov; doi: 10.1126/science.1127647). This method includes a pre
training with the contrastive divergence method published by G.E Hinton
(2002; doi: 10.1162/089976602760128018) and a fine tuning with common known
training algorithms like backpropagation or conjugate gradients.
Additionally, supervised fine-tuning can be enhanced with maxout and
dropout, two recently developed techniques to improve fine-tuning for deep
learning.

Martin Drees

darch

Package for Deep Architectures and Restricted Boltzmann Machines

createDataSet function

Model <code><a rd-options='' href='formula'>formula</a></code>.

<code><a href='<a href='DataSet'></a>'>DataSet</a></code> to be used as the basis for the
new one

createDataSet: Create data set using data, targets, a formula, and possibly an existing data set.

Description

Usage

Arguments

Value

See Also