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<H1>Overview and Examples</H1>
<P>The latest release of Netlab includes the following algorithms:
<UL>
<LI>PCA
<LI>Mixtures of probabilistic PCA
<LI>Gaussian mixture model with EM training algorithm
<LI>Linear and logistic regression with IRLS training algorithm
<LI>Multi-layer perceptron with linear, logistic and softmax outputs and
appropriate error functions
<LI>Radial basis function (RBF) networks with both Gaussian and
non-local basis functions
<LI>Optimisers, including quasi-Newton methods, conjugate gradients and
scaled conjugate gradients
<LI>Multi-layer perceptron with Gaussian mixture outputs (mixture
density networks)
<LI>Gaussian prior distributions over parameters for the MLP, RBF and
GLM including multiple hyper-parameters
<LI>Laplace approximation framework for Bayesian inference (evidence
procedure)
<LI>Automatic Relevance Determination for input selection
<LI>Markov chain Monte-Carlo including simple Metropolis and hybrid
Monte-Carlo
<LI>K-nearest neighbour classifier
<LI>K-means clustering
<LI>Generative Topographic Map
<LI>Neuroscale topographic projection
<LI>Gaussian Processes
<LI>Hinton diagrams for network weights
<LI>Self-organising map </LI></UL>The integration with Matlab means that
powerful facilities are available to pre-process the data, graph important
variables, and visualise results. In addition, Matlab programs that use
Netlab are portable across all main platforms and operating systems
(including UNIX⌨️ 快捷键说明
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