Demo files

Detailed Description

Files
file	cascade_stdhie.m
file	demo_bagging_svm.m
	Bagging example using SVM classifier and drawing classification regions.Uses a 2D artificial data to show the classification boundaries of component classifiers and of the overall classifier.
file	demo_clssr_d.m
	Draws classification regions for classifiers LDC/QDC.
file	demo_clssr_knn.m
	Draws classification regions for classifier k-NN.
file	demo_clssr_tree.m
	Draws classification regions for the Tree classifier.
file	demo_eclass_animation.m
	Shows evolution of classifier eClass, saves animated GIF.Uses userdata_nc2nf2 dataset.
file	demo_eclass_artificial.m
	Draws classification regions for classifier eClass.
file	demo_rater_brain.m
	Classification of Brain data using LDC classifier, leave-one-out cross-validation.
file	demo_u_test_per_wavenumber.m
	U-test per wavenumber is performed for one-versus-one datasets (2-class datasets)
file	demo_bmtable.m
	Biomarkers of Non-transformed vs. Transformed, separated by Chemical.
file	demo_deciders_and_grags.m
	Different ways to use per-spectrum prediction aggregation (grag), and decision thresholds (decider)Generates 4 situations: 2 datasets x 2 analyses.
file	demo_crosscalculated_lda.m
	Cross-calculated LDA scores.
file	demo_pcalda.m
	PCA-LDA demo, scores plots, cluster vectors.
file	demo_as_dsperc_x_rate.m
	(dataset %) x (classification rate %) curve to check sample sizeAllows to verify whether the classification rate would tend to improve if there were more data; or whether apparently there is more data than needed.
file	demo_does_bagging_help.m
file	demo_factorscurve.m
	(number of factors)X(Classification rate) curve
file	demo_feature_histogram_colors.m
	Shows different ways to paint the same Feature Histogram.
file	demo_forward.m
	Forward feature selection demo.
file	demo_gridsearch_knn_and_pca.m
	Combined optimization of PCA number of factors & k-NN k.
file	demo_gridsearch_knn_k.m
	Grid search to obtain best k-NN's k.
file	demo_gridsearch_pca_discriminant.m
	Grid search to simultaneously optimize (PCA number of factors) x ('linear'/'quadratic')
file	demo_eclass_incremental_learning.m
	Increase of classification rate as eClass is incrementally trainedThis example shows how an incremental classifier can vary its performance depending on the order the training data is fed into the classifier.
file	demo_svm_c_gamma.m
	Grid search optimization of SVM (C, gamma) (Gaussian Kernel)
file	demo_classes_html.m
	Generates IRootLab classes hierarchical tree in HTML, using object colors.
file	demo_import_fisheriris.m
	Shows how to assemble a dataset from existing MATLAB matrices (Fisher Iris data example)Loads the "Fisher Iris" dataset that comes with MATLAB Statistics Toolbox.
file	demo_pre_bc_rubber.m
	Demonstrates the Convex Polynomial Line baseline correction.
file	demo_raman_preprocess.m
	Pre-processing of Raman data: Wavelet-De-noising, Polynomial Baseline Correction, Vector Normalization.
file	load_data_hint.m
	Loads the hint dataset: this dataset containg one spectrum only: 1800-900 cm^-1This dataset containg one spectrum only 1800-900 cm^-1.
file	load_data_ketan_brain_atr.m
	Loads Ketan's brain cancer dataset.
file	load_data_matt_nanoparticles_synchrotron.m
	Loads Matt's synchrotron data (5 spectra only)
file	load_data_raman_sample.m
	Loads sample data raman_sample.mat.
file	load_data_she5trays.m
	Loads sample data she5trays.mat.
file	load_data_uci_wine.m
	Loads sample data userdata_nc2nf2.txt.
file	load_data_uglyspectrum.m
	Loads sample data uglyspectrum.mat.
file	load_data_userdata_nc2nf2.m
	Loads sample data userdata_nc2nf2.txt.
file	sampledata_view_all.m
	Plots all sample datasets in separate figures.
file	interactive_bc_poly.m
	Plots polynomial baselines, Helps find order for polynomial-fit baseline correction.
file	data_eliminate_var0.m
	Eliminates low-variance features.