network.txt

有关PPCA的计算程序

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 
Hinton diagrams for network weights 
Self-organising map