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Overview and Examples

The latest release of Netlab includes the following algorithms:

  • PCA
  • Mixtures of probabilistic PCA
  • Gaussian mixture model with EM training algorithm
  • Linear and logistic regression with IRLS training algorithm
  • Multi-layer perceptron with linear, logistic and softmax outputs and appropriate error functions
  • Radial basis function (RBF) networks with both Gaussian and non-local basis functions
  • Optimisers, including quasi-Newton methods, conjugate gradients and scaled conjugate gradients
  • Multi-layer perceptron with Gaussian mixture outputs (mixture density networks)
  • Gaussian prior distributions over parameters for the MLP, RBF and GLM including multiple hyper-parameters
  • Laplace approximation framework for Bayesian inference (evidence procedure)
  • Automatic Relevance Determination for input selection
  • Markov chain Monte-Carlo including simple Metropolis and hybrid Monte-Carlo
  • K-nearest neighbour classifier
  • K-means clustering
  • Generative Topographic Map
  • Neuroscale topographic projection
  • Gaussian Processes including input selection
  • Hinton diagrams for network weights
  • Self-organising map
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®, Microsoft Windows95® and Apple Macintosh® environments).

 

Backwards compatibility

As far as I know, there are only two areas where there are backwards compatibility issues between release 3.2 and earlier releases.

  • Certain networks (of MLP and MDN types) trained under earlier versions of the toolkit will not work with the new functions. This is due to a change of name of one field in the new data structure. It can be corrected by running the Netlab function convertoldnet after the network has been loaded in Matlab. This function works correctly with all networks, both version 3.2 and earlier.
  • The K-nearest-neighbour implementation now involves a data structure (to store the training data) so has separate creation and running functions knn and knnfwd respectively. See demknn for an example of how these should be used.

Documentation is provided in two forms: brief information is provided via the Matlab help system, while a full on-line reference manual is supplied in HTML, which can be read with any suitable browser (such as Netscape®). Netlab is provided with demonstration programs and data sets to illustrate its use on a variety of problems.

Netlab is implemented as a set of functions written in the Matlab language and requires the Matlab environment to run. Matlab is an extendible technical computing environment offering powerful numeric computation and visualisation tools. Netlab uses only core Matlab functions, so is not dependent on any of the optional toolboxes. Most of Netlab will run in Octave, an open source Matlab clone.  The main exception is the Mixture Density Network, although the demo programs don't work very well since gnuplot, the graphing tool used by Octave, is very limited.

Examples

demprior
An example of a demonstration taken from Netlab showing sample neural network functions drawn from a Gaussian prior over weights in which the effect of changing individual hyper-parameters can be explored.

Netlab Example
Contours of the conditional probability density predicted by a mixture density network showing the possibility of multi-modal distributions.

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