PRTools advanced examples
Readers new to PRTools should start by reading the PRTools User Guide.
There is a set of introductory examples that may serve as a part of an introductory PRTools course. These are based on code snippets that can be used by copy-paste-run next to a Matlab window,
Below the growing set of advanced PRTools example files is presented. These are published Matlab m-files that can be run by the user. In various documents references to these files are made.
|A simple Kimia image classification, Introductory PR experiment on blob images. It loads the images, computes features, creates scatter plot and estimates the nearest neighbor classification error.
|Classifiers, Introduction of defining, training and evaluating classifiers.
|Learning curves, Learning curves for Bayes-Normal, Nearest Mean and Nearest Neighbor on the Iris dataset. Averages over 100 repetitions.
|Cross-validation, A large experiment comparing the ability of several cross-validation procedures to determine the best of seven classifiers.
|Feature curves, Feature curves for Bayes-Normal, on the Satellite dataset.
|Feature selection 1, Examples of various feature selection procedures, organized per procedure.
|Feature selection 2, Examples of various feature selection procedures, organized per classifier
|The apparent error, Examples of the behavior of the apparent error for increasing training set size, dimensionality and complexity.
|Bayes classifier uses a 2D examples of four normally distributed classes to show the Bayes classifier and how it can be approximated by qdc. A learning curve shows the speed of convergence to the Bayes error.
|Adaboost introduction, Adaboost 2D examples based on perceptrons and decision stumps.
|Adaboost comparison, Adaboost compared with other base classifier generators and combiners.
|Combining classifiers, Introduction of stacked and parallel combining by fixed and trained combiners.
|Multi-class classifiers, improved by using a trained combiner for post-processing
|PCA versus classifier for feature reduction. An example comparing by feature curves the performance of PCA with that of a trained classifier for feature reduction in a multi-class problem.
|Clustering, Introduction of various clustering techniques.
|Semi-supervised learning by PCA, Illustrates that semi-supervised classification by PCA may be useful.