validateDataSet,DataSet-method

<code><a href="/link/?package=darch&version=0.12.0" dataset'="" data-mini-rdoc="darch::"></a>'>DataSet</code> to validate

dataSet

<code><a href="/link/?package=darch&version=0.12.0" darch'="" data-mini-rdoc="darch::"></a>'>DArch</code> object to validate this 
<code><a href="/link/?package=darch&version=0.12.0" dataset'="" data-mini-rdoc="darch::"></a>'>DataSet</code> against.

darch

Validate <code><a href="/link/?package=darch&version=0.12.0" dataset'="" data-mini-rdoc="darch::"></a>'>DataSet</code>

internal

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 (2006) <DOI:10.1162/neco.2006.18.7.1527>) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov (2006) <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

Package for Deep Architectures and Restricted Boltzmann Machines

validateDataSet,DataSet-method function

<code><a href='<a href='DataSet'></a>'>DataSet</a></code> to validate

<code><a href='<a href='DArch'></a>'>DArch</a></code> object to validate this 
<code><a href='<a href='DataSet'></a>'>DataSet</a></code> against.

Validate <code><a href='<a href='DataSet'></a>'>DataSet</a></code>

validateDataSet,DataSet-method: Validate `'>DataSet`

Description

Usage

Arguments

See Also