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Code Listing by Richard Stapenhurst

Code 1-4 of 4   






This demo gives a clear visual presentation of what happens during the Adaboost algorithms. It shows how the decision boundary, example weights, training error and base learner weights change during training.

A selection of base learning algorithms are included: Linear Regression, Naive Bayes, Decision Stump, CART (requires stats toolbox), Neural Network (requires netlab) and SVM (requires libsvm). There are also 3 dataset...



SVMs are a bit tricky. In this case, we show a linear SVM and illustrate its behaviour on some 2D data. This should be great for getting to grips with maximising geometric margins, support vectors, and the optimisation involved in computing an...



Manipulate moving gaussians in real-time and see how an online learning algorithm reacts.
In non-stationary learning problems, the target distribution changes over time, and our learned model must adapt. In this example, toy data is generated...



Generate 'truth tables' where columns correspond to digits in arbitrary mixtures of bases.
Normal truth tables enumerate all possible rows of binary digits. In a generalised truth table, the base associated with each digit is arbitrary. This...