代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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java compareints.java

// control/CompareInts.java // TIJ4 Chapter Control, Exercise 2, page 139 /* Write a program that generates 25 random int values. For each value, use an * if-else statement to classify it as greate
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www.eeworm.com/read/370596/9592643

java compareints.java

// control/CompareInts.java // TIJ4 Chapter Control, Exercise 2, page 139 /* Write a program that generates 25 random int values. For each value, use an * if-else statement to classify it as greate
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www.eeworm.com/read/415311/11077222

m ho_kashyap.m

function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region) % Classify using the using the Ho-Kashyap algorithm % Inputs: % features - Train features % targets -
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www.eeworm.com/read/415311/11077342

m ml_ii.m

function D = ML_II(train_features, train_targets, Ngaussians, region) % Classify using the ML-II algorithm. This function accepts as inputs the maximum number % of Gaussians per class and returns
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www.eeworm.com/read/414988/11087394

java compareints.java

// control/CompareInts.java // TIJ4 Chapter Control, Exercise 2, page 139 /* Write a program that generates 25 random int values. For each value, use an * if-else statement to classify it as greate
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www.eeworm.com/read/191902/8417108

m id3.m

function D = ID3(train_features, train_targets, params, region) % Classify using Quinlan's ID3 algorithm % Inputs: % features - Train features % targets - Train targets % params - [Number
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www.eeworm.com/read/286662/8751631

m parzen.m

function test_targets = parzen(train_patterns, train_targets, test_patterns, hn) % Classify using the Parzen windows algorithm % Inputs: % train_patterns - Train patterns % train_targets - Trai
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www.eeworm.com/read/286662/8751675

m discrete_bayes.m

function test_targets = Discrete_Bayes(train_patterns, train_targets, test_patterns, cost) % Classify discrete patterns using the Bayes decision theory % Inputs: % train_patterns - Train pattern
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www.eeworm.com/read/286662/8751791

m projection_pursuit.m

function [test_targets, V, Wo] = Projection_Pursuit(train_patterns, train_targets, test_patterns, Ncomponents) % Classify using projection pursuit regression % Inputs: % train_patterns - Train p
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www.eeworm.com/read/286662/8751888

m balanced_winnow.m

function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params) % Classify using the balanced Winnow algorithm % Inputs: % training_patterns -
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