wolffd@0: % ---
wolffd@0: % This script trains similarity measures and shows the
wolffd@0: % results regarding RBM of table 3
wolffd@0: %
wolffd@0: % Feature Preprocessing with RBMs for Music Similarity Learning
wolffd@0: % Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd
wolffd@0: % conference 
wolffd@0: % 
wolffd@0: % please note that the RBM training is a probabilistic process, and 
wolffd@0: % thus the papers' results can only be reproduced approximately with 
wolffd@0: % large numbers of iterations of this script, and selection of RBMs according to
wolffd@0: % their training set performance.
wolffd@0: % Here, training is done on 20 random initialisations of RBM features ,
wolffd@0: % the test results corresponding to the RBM with the best training result are then
wolffd@0: % returned.
wolffd@0: %
wolffd@0: % The train and test performances are output in the console
wolffd@0: %
wolffd@0: % For convenicence, The precomputed RBM features are stored in the files
wolffd@0: % accompaining this script.
wolffd@0: % In order to compute new SVM features, delete these files.
wolffd@0: % ---
wolffd@0: 
wolffd@0: % ---
wolffd@0: % get svm results for RBM
wolffd@0: % ---
wolffd@0: svm_table3
wolffd@0: % svm_test_performance
wolffd@0: %fprintf('SVM Original Test Result (Wolff etal. 2012)=71.20 / 83.54\n');
wolffd@0: 
wolffd@0: % ---
wolffd@0: % get gradient results for RBM
wolffd@0: % ---
wolffd@0: gradient_table3