www.gusucode.com > classification_matlab_toolbox分类方法工具箱源码程序 > code/Classification_toolbox/contents.m
% Classification GUI and toolbox
% Version 1.0
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% GUI start commands
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% classifier - Start the classification GUI
% enter_distributions - Starts the parameter input screen (used by classifier)
% multialgorithms - Start the algorithm comparison screen
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% Preprocessing methods
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% ADDC - Compute k clusters for the data using the agglomerative clustering method
% AGHC - Compute k clusters for the data using the agglomerative hierarchical clustering method
% BIMSEC - Compute k clusters for the data using the basic iterative MSE clustering method
% Competitive_learning - Compute k clusters for the data using a competitive NN
% Deterministic_annealing - Compute k features which typify the data using the Deterministic SA algorithm
% Deterministic_SA - Compute k features which typify the data using the Deterministic SA algorithm (Another implementation)
% DSLVQ - Distinction sensitive linear vector quantization
% Fuzzy_k_means - Compute k means for the data using the fuzzy_k_means algorithm
% FishersLinearDiscriminant - Fisher linear discriminant
% k_means - Compute k means for the data using the k_means algorithm
% Kohonen_SOFM - Reduce data points using a Kohonen self-orgenizing feature map
% Leader_Follower - Compute clusters for the data using the basic leader-follower clustering method
% LVQ1 - Linear vector quantization with one neighbor
% LVQ3 - Linear vector quantization 3 algorithm
% min_spanning_tree - Reduce data points using a minimum spanning tree
% PCA - Principal component analysis
% SOHC - Compute k clusters for the data using the stepwise optimal hierarchical clustering method
% Stochastic_SA - Compute k features which typify the data using the Stochastic SA algorithm
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% Parametric classification algorithms
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% Balanced_Winnow - Balanced Winnow algorithm
% Bayesian_Model_Comparison - Find a Gaussian model using Bayesian model comparison
% EM - Expectation maximization algorithm
% Gibbs - The Gibbs algorithm
% Ho_Kashyap - The regular and modified Ho-Kashyap algorithm
% LMS - Least-means square algorithm
% LS - Least squares algorithm
% Marginalization - Classify when a feature is missing using the marginal distribution
% ML - Maximum likelihood algorithm
% ML_diag - Maximum likelihood with diagonal covariance matrices
% ML_II - Find a Gaussian model using maximum likelihood model comparison
% NDDF - Normal density discriminant function
% None - A dummy file
% Perceptron - Single perceptron algorithm
% Perceptron_Batch - Batch perceptron algorithm
% Perceptron_BVI - Batch variable increment perceptron algorithm
% Perceptron_FM - Perceptron which improves according to the example farthest from the margin
% Perceptron_VIM - Variable increment perceptron with margin algorithm
% Pocket - Pocket algorithm
% RDA - Regularized descriminant analysis (Friedman shrinkage algorithm)
% Relaxation_BM - Batch relaxation with margin
% Relaxation_SSM - Single-sample relaxation with margin
% Stumps - Simple stump classifier
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% Non-parametric classification algorithms
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% Ada_Boost - Ada Boost algorithm
% Backpropagation_Batch - Neural network trained with the batch backpropagation algorithm
% Backpropagation_CGD - Neural network trained with the batch backpropagation algorithm and conjugate gradient descent
% Backpropagation_Quickprop - Neural network trained with the quickprop backpropagation algorithm
% Backpropagation_Recurrent - A recurrent neural network trained with the batch backpropagation algorithm
% Backpropagation_SM - A recurrent neural network trained with the stochastic backpropagation algorithm with momentum
% Backpropagation_Stochastic - Neural network trained with the stochastic backpropagation algorithm
% C4_5 - The C4.5 algorithm
% CART - Classification and regression trees
% CARTfunctions - Used by CART
% Cascade_Correlation - Cascade-correlation type neural network
% Components_with_DF - Component classifiers with descriminant functions
% Components_without_DF - Component classifiers without descriminant functions
% Deterministic_Boltzmann - Deterministic Boltzmann learning
% Discrete_Bayes - Bayes classifier for discrete features
% Genetic_algorithm - Basic genetic algorithm
% Genetic_programming - Genetic programming of a solution
% ID3 - Quinlan's ID3 classification tree algorithm
% Interactive_Learning - Interactive learning (Learning with queries)
% Local_Polynomial - Local polynomial fitting
% loglikelihood - Used by Local polynomial fitting
% LocBoost - Local boosting
% LocBoostFunctions - Used by LocBoost
% Minimum_Cost - Classify under a minimum cost strategy with histogram equalization
% Multivariate_Splines - Multivariate adaptive regression splines
% Nearest_Neighbor - Nearest neighbor algorithm
% NearestNeighborEditing - Nearest neighbor editing algorithm
% Optimal_Brain_Surgeon - Train a backprop. Neural net. and prune it using the optimal brain surgeon algorithm
% Parzen - Parzen window algorithm
% PNN - Probabilistic neural network
% Projection_pursuit - Projection pursuit regression for classification
% RCE - Reduced coulomb energy algorithm
% RBF_Network - Train a radial-basis function neural network
% Store_Grabbag - An improvement on the nearest neighbor algorithm
% SVM - Support vector machines
% Voted perceptron - Voted perceptron algorithm.
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% Feature selection
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% Genetic_Culling - A Culling type genetic algorithm for feature selection
% HDR - Hierarchical dimensionality reduction
% ICA - Independent component analysis
% infomat - Generates the mutual information matrix. Used by Koller
% Koller - Choose the most relevant features using the Koller-Sawami algorithm
% MDS - Multidimensional scaling
% NLPCA - Non-linear PCA
% PCA - Principle component analysis
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% Error estimation
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% calculate_error - Calculates the classification error given a decision surface
% classification_error - Used by claculate_error
% decision_region - Builds a decision region for multi-Gaussian distributions
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% Error bounds
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% Bhattacharyya
% Chernoff
% Discriminability
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% GUI housekeeping functions
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% calculate_region - Finds the data scatter region
% classifier_commands - Classifier screen commands
% click_points - Graphically enter a distribution
% enter_distribution_commands - Used by enter_distributions
% feature_selection - The feature selection GUI open when data with more than 2D is loaded
% feature_selection_commands - The commands file for the feature selection GUI
% find_classes - Find which classes exist in a data set
% FindParameters - A GUI for finding the optimal parameters for a classifier
% FindParametersFunctions - The commands file for FindParameters
% GaussianParameters - Opens a GUI for displaying the gaussian parameters of a distribution
% generate_data_set - Generate a data set given Gaussian parameters
% high_histogram - Generate a histogram for high-dimensional data
% load_file - Load data files
% make_a_draw - Randomly find indices from a data set
% multialgorithms_commands - Multialgorithms screen comands
% plot_process - Plot partition centers during the algorithm execution
% plot_scatter - Make a scatter plot of a data set
% Predict_performance - Predict performance of algorithms from their learning curves
% process_params - Read a parameter vector and return it's components
% read_algorithms - Reads an algorithm file into a data structure
% start_classify - Main function used by classifier
% voronoi_regions - Plot Voronoi regions
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% Data sets (Ending _data means that the file contains features,
% _params means that the file contains the distribution parameters)
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% chess - The parameters for a 4x4 chess board distribution
% clouds - A data set composed of four Gaussians
% seperable - A linearly seperable data set
% spiral - Two interlocking spirals data set
% XOR - XOR distribution
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%____________________________________________________________________________________
% Elad Yom-Tov (elad@ieee.org) and David Stork
% Technion - Israel Institute of Technology
% Haifa, Israel