代码搜索：Classify

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www.eeworm.com/read/245941/12771218

m components_with_df.m

function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents) % Classify points using component classifiers with discriminant functions % Inputs:
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www.eeworm.com/read/330850/12864707

m ml_diag.m

function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters) % Classify using the maximum likelyhood algorithm with diagonal covariance matrices % Inputs: %
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www.eeworm.com/read/330850/12865199

m components_with_df.m

function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents) % Classify points using component classifiers with discriminant functions % Inputs:
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www.eeworm.com/read/142729/12929561

m script_text.m

% script to classify text using ICA % by Thomas Kolenda DTU,IMM 2000,2002 version 2 close all clear all format compact % settings ClassFrac=0; % Reject frac for classification
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www.eeworm.com/read/325774/13185749

m identifyingroundobjects.m

%Identifying Round Objects %Your goal is to classify objects based on their roundness using %bwboundaries, a boundary tracing routine. %Step 1: Read image RGB = imread('pillsetc.png'); imshow(R
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www.eeworm.com/read/317622/13500811

m ml_diag.m

function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters) % Classify using the maximum likelyhood algorithm with diagonal covariance matrices % Inputs: %
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www.eeworm.com/read/317622/13500966

m components_with_df.m

function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents) % Classify points using component classifiers with discriminant functions % Inputs:
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www.eeworm.com/read/316604/13520390

m backpropagation_batch.m

function [D, Wh, Wo] = Backpropagation_Batch(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm % Inputs: % features- Train
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www.eeworm.com/read/316604/13520391

m cascade_correlation.m

function D = Cascade_Correlation(train_features, train_targets, params, region) % Classify using a backpropagation network with the cascade-correlation algorithm % Inputs: % features- Train feat
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www.eeworm.com/read/316604/13520392

m nearest_neighbor.m

function D = Nearest_Neighbor(train_features, train_targets, Knn, region) % Classify using the Nearest neighbor algorithm % Inputs: % features - Train features % targets - Train targets % Knn
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