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www.eeworm.com/read/212307/15160113

m demknn1.m

%DEMKNN1 Demonstrate nearest neighbour classifier. % % Description % The problem consists of data in a two-dimensional space. The data is % drawn from three spherical Gaussian distributions with prio
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www.eeworm.com/read/212307/15160195

m demmlp2.m

%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron % % Description % The problem consists of input data in two dimensions drawn from a % mixture of three Gaussians: two of which
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www.eeworm.com/read/211966/15169309

c panic.c

/* * linux/kernel/panic.c * * Copyright (C) 1991, 1992 Linus Torvalds */ /* * This function is used through-out the kernel (including mm and fs) * to indicate a major problem. */ #include
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www.eeworm.com/read/211875/15171906

changes

Changes from version 1.0.0 to 1.0.1 - Added copyright lines in all files - Added README file - Added Changes file -- :) - Added shortprint/ directory (we forgot about it) - Fixed problem with sc
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www.eeworm.com/read/209231/15225266

svn-base plot2dkm.m.svn-base

% PLOT2DKM - For a 2-D binary classification problem, plot2dkm plots the data, % the margin and error vectors and contours of constant margin % for the SVM classifier in memor
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www.eeworm.com/read/209231/15225289

m plot2dkm.m

% PLOT2DKM - For a 2-D binary classification problem, plot2dkm plots the data, % the margin and error vectors and contours of constant margin % for the SVM classifier in memor
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www.eeworm.com/read/208225/15250562

changes

Changes from version 1.0.0 to 1.0.1 - Added copyright lines in all files - Added README file - Added Changes file -- :) - Added shortprint/ directory (we forgot about it) - Fixed problem with sc
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www.eeworm.com/read/13871/284475

m hungarian.m

function [C,T]=hungarian(A) %HUNGARIAN Solve the Assignment problem using the Hungarian method. % %[C,T]=hungarian(A) %A - a square cost matrix. %C - the optimal assignment. %T - the cost of the
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www.eeworm.com/read/13871/284509

m demhmc3.m

%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by samplin
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www.eeworm.com/read/13871/284520

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampli
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