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+<html>
+<head>
+<title>
+NETLAB Reference Documentation 
+</title>
+</head>
+<body>
+<H1> NETLAB Online Reference Documentation </H1>
+Welcome to the NETLAB online reference documentation.
+The NETLAB simulation software is designed to provide all the tools necessary
+for principled and theoretically well founded application development. The
+NETLAB library is based on the approach and techniques described in <I>Neural
+Networks for Pattern Recognition </I>(Bishop, 1995). The library includes software
+implementations of a wide range of data analysis techniques, many of which are
+not widely available, and are rarely, if ever, included in standard neural
+network simulation packages.
+<p>The online reference documentation provides direct hypertext links to specific Netlab function descriptions.
+<p>If you have any comments or problems to report, please contact Ian Nabney (<a href="mailto:i.t.nabney@aston.ac.uk"><tt>i.t.nabney@aston.ac.uk</tt></a>) or Christopher Bishop (<a href="mailto:c.m.bishop@aston.ac.uk"><tt>c.m.bishop@aston.ac.uk</tt></a>).<H1> Index
+</H1>
+An alphabetic list of functions in Netlab.<p>
+<DL>
+<DT>
+<CODE><a href="conffig.htm">conffig</a></CODE><DD>
+ Display a confusion matrix. 
+<DT>
+<CODE><a href="confmat.htm">confmat</a></CODE><DD>
+ Compute a confusion matrix. 
+<DT>
+<CODE><a href="conjgrad.htm">conjgrad</a></CODE><DD>
+ Conjugate gradients optimization. 
+<DT>
+<CODE><a href="consist.htm">consist</a></CODE><DD>
+ Check that arguments are consistent. 
+<DT>
+<CODE><a href="convertoldnet.htm">convertoldnet</a></CODE><DD>
+ Convert pre-2.3 release MLP and MDN nets to new format 
+<DT>
+<CODE><a href="datread.htm">datread</a></CODE><DD>
+ Read data from an ascii file. 
+<DT>
+<CODE><a href="datwrite.htm">datwrite</a></CODE><DD>
+ Write data to ascii file. 
+<DT>
+<CODE><a href="dem2ddat.htm">dem2ddat</a></CODE><DD>
+ Generates two dimensional data for demos. 
+<DT>
+<CODE><a href="demard.htm">demard</a></CODE><DD>
+ Automatic relevance determination using the MLP. 
+<DT>
+<CODE><a href="demev1.htm">demev1</a></CODE><DD>
+ Demonstrate Bayesian regression for the MLP. 
+<DT>
+<CODE><a href="demev2.htm">demev2</a></CODE><DD>
+ Demonstrate Bayesian classification for the MLP. 
+<DT>
+<CODE><a href="demev3.htm">demev3</a></CODE><DD>
+ Demonstrate Bayesian regression for the RBF. 
+<DT>
+<CODE><a href="demgauss.htm">demgauss</a></CODE><DD>
+ Demonstrate sampling from Gaussian distributions. 
+<DT>
+<CODE><a href="demglm1.htm">demglm1</a></CODE><DD>
+ Demonstrate simple classification using a generalized linear model. 
+<DT>
+<CODE><a href="demglm2.htm">demglm2</a></CODE><DD>
+ Demonstrate simple classification using a generalized linear model. 
+<DT>
+<CODE><a href="demgmm1.htm">demgmm1</a></CODE><DD>
+ Demonstrate density modelling with a Gaussian mixture model. 
+<DT>
+<CODE><a href="demgmm3.htm">demgmm3</a></CODE><DD>
+ Demonstrate density modelling with a Gaussian mixture model. 
+<DT>
+<CODE><a href="demgmm4.htm">demgmm4</a></CODE><DD>
+ Demonstrate density modelling with a Gaussian mixture model. 
+<DT>
+<CODE><a href="demgmm5.htm">demgmm5</a></CODE><DD>
+ Demonstrate density modelling with a PPCA mixture model. 
+<DT>
+<CODE><a href="demgp.htm">demgp</a></CODE><DD>
+ Demonstrate simple regression using a Gaussian Process. 
+<DT>
+<CODE><a href="demgpard.htm">demgpard</a></CODE><DD>
+ Demonstrate ARD using a Gaussian Process. 
+<DT>
+<CODE><a href="demgpot.htm">demgpot</a></CODE><DD>
+ Computes the gradient of the negative log likelihood for a mixture model. 
+<DT>
+<CODE><a href="demgtm1.htm">demgtm1</a></CODE><DD>
+ Demonstrate EM for GTM. 
+<DT>
+<CODE><a href="demgtm2.htm">demgtm2</a></CODE><DD>
+ Demonstrate GTM for visualisation. 
+<DT>
+<CODE><a href="demhint.htm">demhint</a></CODE><DD>
+ Demonstration of Hinton diagram for 2-layer feed-forward network. 
+<DT>
+<CODE><a href="demhmc1.htm">demhmc1</a></CODE><DD>
+ Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. 
+<DT>
+<CODE><a href="demhmc2.htm">demhmc2</a></CODE><DD>
+ Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. 
+<DT>
+<CODE><a href="demhmc3.htm">demhmc3</a></CODE><DD>
+ Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. 
+<DT>
+<CODE><a href="demkmean.htm">demkmean</a></CODE><DD>
+ Demonstrate simple clustering model trained with K-means. 
+<DT>
+<CODE><a href="demknn1.htm">demknn1</a></CODE><DD>
+ Demonstrate nearest neighbour classifier. 
+<DT>
+<CODE><a href="demmdn1.htm">demmdn1</a></CODE><DD>
+ Demonstrate fitting a multi-valued function using a Mixture Density Network. 
+<DT>
+<CODE><a href="demmet1.htm">demmet1</a></CODE><DD>
+ Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. 
+<DT>
+<CODE><a href="demmlp1.htm">demmlp1</a></CODE><DD>
+ Demonstrate simple regression using a multi-layer perceptron 
+<DT>
+<CODE><a href="demmlp2.htm">demmlp2</a></CODE><DD>
+ Demonstrate simple classification using a multi-layer perceptron 
+<DT>
+<CODE><a href="demnlab.htm">demnlab</a></CODE><DD>
+ A front-end Graphical User Interface to the demos 
+<DT>
+<CODE><a href="demns1.htm">demns1</a></CODE><DD>
+ Demonstrate Neuroscale for visualisation. 
+<DT>
+<CODE><a href="demolgd1.htm">demolgd1</a></CODE><DD>
+ Demonstrate simple MLP optimisation with on-line gradient descent 
+<DT>
+<CODE><a href="demopt1.htm">demopt1</a></CODE><DD>
+ Demonstrate different optimisers on Rosenbrock's function. 
+<DT>
+<CODE><a href="dempot.htm">dempot</a></CODE><DD>
+ Computes the negative log likelihood for a mixture model. 
+<DT>
+<CODE><a href="demprgp.htm">demprgp</a></CODE><DD>
+ Demonstrate sampling from a Gaussian Process prior. 
+<DT>
+<CODE><a href="demprior.htm">demprior</a></CODE><DD>
+ Demonstrate sampling from a multi-parameter Gaussian prior. 
+<DT>
+<CODE><a href="demrbf1.htm">demrbf1</a></CODE><DD>
+ Demonstrate simple regression using a radial basis function network. 
+<DT>
+<CODE><a href="demsom1.htm">demsom1</a></CODE><DD>
+ Demonstrate SOM for visualisation. 
+<DT>
+<CODE><a href="demtrain.htm">demtrain</a></CODE><DD>
+ Demonstrate training of MLP network. 
+<DT>
+<CODE><a href="dist2.htm">dist2</a></CODE><DD>
+ Calculates squared distance between two sets of points. 
+<DT>
+<CODE><a href="eigdec.htm">eigdec</a></CODE><DD>
+ Sorted eigendecomposition 
+<DT>
+<CODE><a href="errbayes.htm">errbayes</a></CODE><DD>
+ Evaluate Bayesian error function for network. 
+<DT>
+<CODE><a href="evidence.htm">evidence</a></CODE><DD>
+ Re-estimate hyperparameters using evidence approximation. 
+<DT>
+<CODE><a href="fevbayes.htm">fevbayes</a></CODE><DD>
+ Evaluate Bayesian regularisation for network forward propagation. 
+<DT>
+<CODE><a href="gauss.htm">gauss</a></CODE><DD>
+ Evaluate a Gaussian distribution. 
+<DT>
+<CODE><a href="gbayes.htm">gbayes</a></CODE><DD>
+ Evaluate gradient of Bayesian error function for network. 
+<DT>
+<CODE><a href="glm.htm">glm</a></CODE><DD>
+ Create a generalized linear model. 
+<DT>
+<CODE><a href="glmderiv.htm">glmderiv</a></CODE><DD>
+ Evaluate derivatives of GLM outputs with respect to weights. 
+<DT>
+<CODE><a href="glmerr.htm">glmerr</a></CODE><DD>
+ Evaluate error function for generalized linear model. 
+<DT>
+<CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><DD>
+ Forward propagation with evidence for GLM 
+<DT>
+<CODE><a href="glmfwd.htm">glmfwd</a></CODE><DD>
+ Forward propagation through generalized linear model. 
+<DT>
+<CODE><a href="glmgrad.htm">glmgrad</a></CODE><DD>
+ Evaluate gradient of error function for generalized linear model. 
+<DT>
+<CODE><a href="glmhess.htm">glmhess</a></CODE><DD>
+ Evaluate the Hessian matrix for a generalised linear model. 
+<DT>
+<CODE><a href="glminit.htm">glminit</a></CODE><DD>
+ Initialise the weights in a generalized linear model. 
+<DT>
+<CODE><a href="glmpak.htm">glmpak</a></CODE><DD>
+ Combines weights and biases into one weights vector. 
+<DT>
+<CODE><a href="glmtrain.htm">glmtrain</a></CODE><DD>
+ Specialised training of generalized linear model 
+<DT>
+<CODE><a href="glmunpak.htm">glmunpak</a></CODE><DD>
+ Separates weights vector into weight and bias matrices. 
+<DT>
+<CODE><a href="gmm.htm">gmm</a></CODE><DD>
+ Creates a Gaussian mixture model with specified architecture. 
+<DT>
+<CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><DD>
+ Computes the activations of a Gaussian mixture model. 
+<DT>
+<CODE><a href="gmmem.htm">gmmem</a></CODE><DD>
+ EM algorithm for Gaussian mixture model. 
+<DT>
+<CODE><a href="gmminit.htm">gmminit</a></CODE><DD>
+ Initialises Gaussian mixture model from data 
+<DT>
+<CODE><a href="gmmpak.htm">gmmpak</a></CODE><DD>
+ Combines all the parameters in a Gaussian mixture model into one vector. 
+<DT>
+<CODE><a href="gmmpost.htm">gmmpost</a></CODE><DD>
+ Computes the class posterior probabilities of a Gaussian mixture model. 
+<DT>
+<CODE><a href="gmmprob.htm">gmmprob</a></CODE><DD>
+ Computes the data probability for a Gaussian mixture model. 
+<DT>
+<CODE><a href="gmmsamp.htm">gmmsamp</a></CODE><DD>
+ Sample from a Gaussian mixture distribution. 
+<DT>
+<CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><DD>
+ Separates a vector of Gaussian mixture model parameters into its components. 
+<DT>
+<CODE><a href="gp.htm">gp</a></CODE><DD>
+ Create a Gaussian Process. 
+<DT>
+<CODE><a href="gpcovar.htm">gpcovar</a></CODE><DD>
+ Calculate the covariance for a Gaussian Process. 
+<DT>
+<CODE><a href="gpcovarf.htm">gpcovarf</a></CODE><DD>
+ Calculate the covariance function for a Gaussian Process. 
+<DT>
+<CODE><a href="gpcovarp.htm">gpcovarp</a></CODE><DD>
+ Calculate the prior covariance for a Gaussian Process. 
+<DT>
+<CODE><a href="gperr.htm">gperr</a></CODE><DD>
+ Evaluate error function for Gaussian Process. 
+<DT>
+<CODE><a href="gpfwd.htm">gpfwd</a></CODE><DD>
+ Forward propagation through Gaussian Process. 
+<DT>
+<CODE><a href="gpgrad.htm">gpgrad</a></CODE><DD>
+ Evaluate error gradient for Gaussian Process. 
+<DT>
+<CODE><a href="gpinit.htm">gpinit</a></CODE><DD>
+ Initialise Gaussian Process model. 
+<DT>
+<CODE><a href="gppak.htm">gppak</a></CODE><DD>
+ Combines GP hyperparameters into one vector. 
+<DT>
+<CODE><a href="gpunpak.htm">gpunpak</a></CODE><DD>
+ Separates hyperparameter vector into components. 
+<DT>
+<CODE><a href="gradchek.htm">gradchek</a></CODE><DD>
+ Checks a user-defined gradient function using finite differences. 
+<DT>
+<CODE><a href="graddesc.htm">graddesc</a></CODE><DD>
+ Gradient descent optimization. 
+<DT>
+<CODE><a href="gsamp.htm">gsamp</a></CODE><DD>
+ Sample from a Gaussian distribution. 
+<DT>
+<CODE><a href="gtm.htm">gtm</a></CODE><DD>
+ Create a Generative Topographic Map. 
+<DT>
+<CODE><a href="gtmem.htm">gtmem</a></CODE><DD>
+ EM algorithm for Generative Topographic Mapping. 
+<DT>
+<CODE><a href="gtmfwd.htm">gtmfwd</a></CODE><DD>
+ Forward propagation through GTM. 
+<DT>
+<CODE><a href="gtminit.htm">gtminit</a></CODE><DD>
+ Initialise the weights and latent sample in a GTM. 
+<DT>
+<CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><DD>
+ Mean responsibility for data in a GTM. 
+<DT>
+<CODE><a href="gtmlmode.htm">gtmlmode</a></CODE><DD>
+ Mode responsibility for data in a GTM. 
+<DT>
+<CODE><a href="gtmmag.htm">gtmmag</a></CODE><DD>
+ Magnification factors for a GTM 
+<DT>
+<CODE><a href="gtmpost.htm">gtmpost</a></CODE><DD>
+ Latent space responsibility for data in a GTM. 
+<DT>
+<CODE><a href="gtmprob.htm">gtmprob</a></CODE><DD>
+ Probability for data under a GTM. 
+<DT>
+<CODE><a href="hbayes.htm">hbayes</a></CODE><DD>
+ Evaluate Hessian of Bayesian error function for network. 
+<DT>
+<CODE><a href="hesschek.htm">hesschek</a></CODE><DD>
+ Use central differences to confirm correct evaluation of Hessian matrix. 
+<DT>
+<CODE><a href="hintmat.htm">hintmat</a></CODE><DD>
+ Evaluates the coordinates of the patches for a Hinton diagram. 
+<DT>
+<CODE><a href="hinton.htm">hinton</a></CODE><DD>
+ Plot Hinton diagram for a weight matrix. 
+<DT>
+<CODE><a href="histp.htm">histp</a></CODE><DD>
+ Histogram estimate of 1-dimensional probability distribution. 
+<DT>
+<CODE><a href="hmc.htm">hmc</a></CODE><DD>
+ Hybrid Monte Carlo sampling. 
+<DT>
+<CODE><a href="kmeans.htm">kmeans</a></CODE><DD>
+ Trains a k means cluster model. 
+<DT>
+<CODE><a href="knn.htm">knn</a></CODE><DD>
+ Creates a K-nearest-neighbour classifier. 
+<DT>
+<CODE><a href="knnfwd.htm">knnfwd</a></CODE><DD>
+ Forward propagation through a K-nearest-neighbour classifier. 
+<DT>
+<CODE><a href="linef.htm">linef</a></CODE><DD>
+ Calculate function value along a line. 
+<DT>
+<CODE><a href="linemin.htm">linemin</a></CODE><DD>
+ One dimensional minimization. 
+<DT>
+<CODE><a href="maxitmess.htm">maxitmess</a></CODE><DD>
+ Create a standard error message when training reaches max. iterations. 
+<DT>
+<CODE><a href="mdn.htm">mdn</a></CODE><DD>
+ Creates a Mixture Density Network with specified architecture. 
+<DT>
+<CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE><DD>
+ Converts an MDN mixture data structure to array of GMMs. 
+<DT>
+<CODE><a href="mdndist2.htm">mdndist2</a></CODE><DD>
+ Calculates squared distance between centres of Gaussian kernels and data 
+<DT>
+<CODE><a href="mdnerr.htm">mdnerr</a></CODE><DD>
+ Evaluate error function for Mixture Density Network. 
+<DT>
+<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><DD>
+ Forward propagation through Mixture Density Network. 
+<DT>
+<CODE><a href="mdngrad.htm">mdngrad</a></CODE><DD>
+ Evaluate gradient of error function for Mixture Density Network. 
+<DT>
+<CODE><a href="mdninit.htm">mdninit</a></CODE><DD>
+ Initialise the weights in a Mixture Density Network. 
+<DT>
+<CODE><a href="mdnpak.htm">mdnpak</a></CODE><DD>
+ Combines weights and biases into one weights vector. 
+<DT>
+<CODE><a href="mdnpost.htm">mdnpost</a></CODE><DD>
+ Computes the posterior probability for each MDN mixture component. 
+<DT>
+<CODE><a href="mdnprob.htm">mdnprob</a></CODE><DD>
+ Computes the data probability likelihood for an MDN mixture structure. 
+<DT>
+<CODE><a href="mdnunpak.htm">mdnunpak</a></CODE><DD>
+ Separates weights vector into weight and bias matrices. 
+<DT>
+<CODE><a href="metrop.htm">metrop</a></CODE><DD>
+ Markov Chain Monte Carlo sampling with Metropolis algorithm. 
+<DT>
+<CODE><a href="minbrack.htm">minbrack</a></CODE><DD>
+ Bracket a minimum of a function of one variable. 
+<DT>
+<CODE><a href="mlp.htm">mlp</a></CODE><DD>
+ Create a 2-layer feedforward network. 
+<DT>
+<CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><DD>
+ Backpropagate gradient of error function for 2-layer network. 
+<DT>
+<CODE><a href="mlpderiv.htm">mlpderiv</a></CODE><DD>
+ Evaluate derivatives of network outputs with respect to weights. 
+<DT>
+<CODE><a href="mlperr.htm">mlperr</a></CODE><DD>
+ Evaluate error function for 2-layer network. 
+<DT>
+<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE><DD>
+ Forward propagation with evidence for MLP 
+<DT>
+<CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><DD>
+ Forward propagation through 2-layer network. 
+<DT>
+<CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><DD>
+ Evaluate gradient of error function for 2-layer network. 
+<DT>
+<CODE><a href="mlphdotv.htm">mlphdotv</a></CODE><DD>
+ Evaluate the product of the data Hessian with a vector. 
+<DT>
+<CODE><a href="mlphess.htm">mlphess</a></CODE><DD>
+ Evaluate the Hessian matrix for a multi-layer perceptron network. 
+<DT>
+<CODE><a href="mlphint.htm">mlphint</a></CODE><DD>
+ Plot Hinton diagram for 2-layer feed-forward network. 
+<DT>
+<CODE><a href="mlpinit.htm">mlpinit</a></CODE><DD>
+ Initialise the weights in a 2-layer feedforward network. 
+<DT>
+<CODE><a href="mlppak.htm">mlppak</a></CODE><DD>
+ Combines weights and biases into one weights vector. 
+<DT>
+<CODE><a href="mlpprior.htm">mlpprior</a></CODE><DD>
+ Create Gaussian prior for mlp. 
+<DT>
+<CODE><a href="mlptrain.htm">mlptrain</a></CODE><DD>
+ Utility to train an MLP network for demtrain 
+<DT>
+<CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><DD>
+ Separates weights vector into weight and bias matrices. 
+<DT>
+<CODE><a href="netderiv.htm">netderiv</a></CODE><DD>
+ Evaluate derivatives of network outputs by weights generically. 
+<DT>
+<CODE><a href="neterr.htm">neterr</a></CODE><DD>
+ Evaluate network error function for generic optimizers 
+<DT>
+<CODE><a href="netevfwd.htm">netevfwd</a></CODE><DD>
+ Generic forward propagation with evidence for network 
+<DT>
+<CODE><a href="netgrad.htm">netgrad</a></CODE><DD>
+ Evaluate network error gradient for generic optimizers 
+<DT>
+<CODE><a href="nethess.htm">nethess</a></CODE><DD>
+ Evaluate network Hessian 
+<DT>
+<CODE><a href="netinit.htm">netinit</a></CODE><DD>
+ Initialise the weights in a network. 
+<DT>
+<CODE><a href="netopt.htm">netopt</a></CODE><DD>
+ Optimize the weights in a network model. 
+<DT>
+<CODE><a href="netpak.htm">netpak</a></CODE><DD>
+ Combines weights and biases into one weights vector. 
+<DT>
+<CODE><a href="netunpak.htm">netunpak</a></CODE><DD>
+ Separates weights vector into weight and bias matrices. 
+<DT>
+<CODE><a href="olgd.htm">olgd</a></CODE><DD>
+ On-line gradient descent optimization. 
+<DT>
+<CODE><a href="pca.htm">pca</a></CODE><DD>
+ Principal Components Analysis 
+<DT>
+<CODE><a href="plotmat.htm">plotmat</a></CODE><DD>
+ Display a matrix. 
+<DT>
+<CODE><a href="ppca.htm">ppca</a></CODE><DD>
+ Probabilistic Principal Components Analysis 
+<DT>
+<CODE><a href="quasinew.htm">quasinew</a></CODE><DD>
+ Quasi-Newton optimization. 
+<DT>
+<CODE><a href="rbf.htm">rbf</a></CODE><DD>
+ Creates an RBF network with specified architecture 
+<DT>
+<CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><DD>
+ Backpropagate gradient of error function for RBF network. 
+<DT>
+<CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><DD>
+ Evaluate derivatives of RBF network outputs with respect to weights. 
+<DT>
+<CODE><a href="rbferr.htm">rbferr</a></CODE><DD>
+ Evaluate error function for RBF network. 
+<DT>
+<CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE><DD>
+ Forward propagation with evidence for RBF 
+<DT>
+<CODE><a href="rbffwd.htm">rbffwd</a></CODE><DD>
+ Forward propagation through RBF network with linear outputs. 
+<DT>
+<CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><DD>
+ Evaluate gradient of error function for RBF network. 
+<DT>
+<CODE><a href="rbfhess.htm">rbfhess</a></CODE><DD>
+ Evaluate the Hessian matrix for RBF network. 
+<DT>
+<CODE><a href="rbfjacob.htm">rbfjacob</a></CODE><DD>
+ Evaluate derivatives of RBF network outputs with respect to inputs. 
+<DT>
+<CODE><a href="rbfpak.htm">rbfpak</a></CODE><DD>
+ Combines all the parameters in an RBF network into one weights vector. 
+<DT>
+<CODE><a href="rbfprior.htm">rbfprior</a></CODE><DD>
+ Create Gaussian prior and output layer mask for RBF. 
+<DT>
+<CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE><DD>
+ Set basis functions of RBF from data. 
+<DT>
+<CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE><DD>
+ Set basis function widths of RBF. 
+<DT>
+<CODE><a href="rbftrain.htm">rbftrain</a></CODE><DD>
+ Two stage training of RBF network. 
+<DT>
+<CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><DD>
+ Separates a vector of RBF weights into its components. 
+<DT>
+<CODE><a href="rosegrad.htm">rosegrad</a></CODE><DD>
+ Calculate gradient of Rosenbrock's function. 
+<DT>
+<CODE><a href="rosen.htm">rosen</a></CODE><DD>
+ Calculate Rosenbrock's function. 
+<DT>
+<CODE><a href="scg.htm">scg</a></CODE><DD>
+ Scaled conjugate gradient optimization. 
+<DT>
+<CODE><a href="som.htm">som</a></CODE><DD>
+ Creates a Self-Organising Map. 
+<DT>
+<CODE><a href="somfwd.htm">somfwd</a></CODE><DD>
+ Forward propagation through a Self-Organising Map. 
+<DT>
+<CODE><a href="sompak.htm">sompak</a></CODE><DD>
+ Combines node weights into one weights matrix. 
+<DT>
+<CODE><a href="somtrain.htm">somtrain</a></CODE><DD>
+ Kohonen training algorithm for SOM. 
+<DT>
+<CODE><a href="somunpak.htm">somunpak</a></CODE><DD>
+ Replaces node weights in SOM. 
+</DL>
+
+<hr>
+<p>Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
+</body>
+</html>
\ No newline at end of file