Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

nn

addr
creates one hidden layer network
ann
a tool to run a feed-forward neural network
ann2
is a tool to run a feed-forward neural network
committee
This quantlet computes a committee of networks with nets of the form single layer feedforward perceptron. The quantlet can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. The number of nets to build the committ
committee2
This quantlet computes a committee of networks with nets of the form single layer feedforward perceptron. The quantlet can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. The number of nets to build the committ
cv
runs a cross validation over the hidden units
cv2
runs a cross validation over the hidden units
cvdec
runs a cross validation over the weight decay
cvdec2
runs a cross validation over the weight decay
erfkl
Kullback-Leibler criterion for classification
erfqua
(1-R^2) criterion for regression
finalshow
shows the final visualization of the network
finalshow2
shows the final visualization of the network
gennet
generates interactively a feedforward network
gennet2
generates interactively a feedforward network
neuronal
This quantlet computes different networks of the form single layer feedforward perceptron. The quantlet can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. It is possible to split the data in a training and a t
neuronal2
This macro computes different networks of the form single layer feedforward perceptron. The macro can be used alone or in connection with the library ISTA. The standalone version also needs the parameter data. Just choose 0 for the input. It is possible to split the data in a training and a test se
nnfunc2
nnfunc2 computes for a given feed forward network the result for a datavector x.
nninfo
shows some information about the actual network
nninit2
nninit2 checks if a given network is feedforward network and suggest reorderings.
nnlayer
builds a feedforward network
nnmain
loads the necessary libraries
nnrdovm2
nnrdovm2 optimizes a network for a given dataset.
nnrinfo
gives information about the net
nnrload
loads a network from different files
nnrnet
trains a one hidden layer feed forward network. The optional parameter param consists of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections. The fifth value is the maximum value for the starting weights, the sixth the weight decay, th
nnrnet2
trains a one hidden layer feed forward network. The optional parameter param consists of 8 values. Boolean values for linear output, entropy error function, log probability models and for skip connections. The fifth value is the maximum value for the starting weights, the sixth the weight decay, th
nnrpredict
estimates the response for a given net and a dataset
nnrpredict2
estimates the response for a given net and dataset
nnrsave
saves a network into a file with a given name
nnrsetnet2
nnrsetnet2 sets the internal variables to construct a specific network.
nnrsettrain2
nnrsettrain2 sets the internal variables to fill a specific network with data and weights.
nnrtest2
nnrtest2 computes for a given network and dataset the y-values and the Hessian.
nnvisu2
nnvisu2 computes the visualization for a given feed forward network by non-metric multidimensional scaling.
optdec
runs for each set of observations a neural network to estimate the generalization error
optdec2
runs for each set of observations a neural network to estimate the generalization error
rbfinfo
shows information about the given network
rbfload
loads a saved RBF network from a file
rbfpredict
predicts the output of given RBF neural network
rbfsave
saves given radial basis function network into the given file
rbftest
tests the given rbfnet network
rbftrain
trains a radial basis function neural network
rbftrain2
trains a radial basis function neural network
readshow
shows the visualization of a feedforward neural network
readshow2
shows the visualization of a feedforward neural network
resclass
shows the residuals in case of the classification
resclass2
shows the residuals in case of the classification
resreg
shows the residuals in case of the regression
resreg2
shows the residuals in case of the regression
runcv
runs a cross validation and estimates the generalization error
runcv2
runs a cross validation and estimates the generalization error
runinit
initializes the training andtest dataset, the errors and the weights in the network
runinit2
initializes the training andtest dataset, the errors and the weights in the network
runnet
runs a network with prespecified optimization method
runnet2
runs a network with prespecified optimization method
runnew
optimize a neural network by a quadratic approximation
runnew2
optimize a neural network by a quadratic approximation
runqsa
optimizes a neural network by a stochastic search
runqsa2
optimizes a neural network by a stochastic search
runsa
optimizes a neural network by Boltzman annealing
runsa2
optimizes a neural network by Boltzman annealing
runshow
visualizes a neural network during optimization
runshow2
visualizes a neural network during optimization
weidist1
transforms weights in distances (\delta^{(2)})
weidist2
transforms weights in distances (\delta^{(2)})
weidist3
transforms weights in distances
weinit
initializes the weights of a neural network
x3matrix
constructs a matrix link in XploRe 3

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 05.02.2006Impressum