index.htm

模式识别的主要工具集合

 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>

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<p>Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
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