代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

代码结果 2,639
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www.eeworm.com/read/410924/11264971

m backpropagation_stochastic.m

function [D, Wh, Wo] = Backpropagation_Stochastic(train_features, train_targets, params, region) % Classify using a backpropagation network with stochastic learning algorithm % Inputs: % feature
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www.eeworm.com/read/410924/11265014

m cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % para
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www.eeworm.com/read/410924/11265056

m marginalization.m

function D = Marginalization(train_features, train_targets, missing, region) % Classify data with missing features using the marginal distribution % This file is strongly made for only two feature
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www.eeworm.com/read/404974/11474809

bp sonar.bp

* This program runs the aspect-angle dependent data from Gorman and * Sejnowski's article: "Analysis of Hidden Units in a Layered Network * Trained to Classify Sonar Targets", in Neural Networks, vo
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www.eeworm.com/read/132026/14113423

txt 数据挖掘中cart算法实现.txt

CART function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targe
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www.eeworm.com/read/131588/14136149

m parzen.m

function D = parzen(train_features, train_targets, hn, region) % Classify using the Parzen windows algorithm % Inputs: % features - Train features % targets - Train targets % hn - No
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www.eeworm.com/read/131588/14136150

m ml_diag.m

function D = ML_diag(train_features, train_targets, AlgorithmParameters, region) % Classify using the maximum likelyhood algorithm with diagonal covariance matrices % Inputs: % features - Train
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www.eeworm.com/read/131588/14136165

m multivariate_splines.m

function D = Multivariate_Splines(train_features, train_targets, params, region) % Classify using multivariate adaptive regression splines % Inputs: % features - Train features % targets -
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www.eeworm.com/read/131588/14136172

m deterministic_boltzmann.m

function D = Deterministic_Boltzmann(train_features, train_targets, params, region); % Classify using the deterministic Boltzmann algorithm % Inputs: % features - Train features % targets - Tra
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www.eeworm.com/read/131588/14136182

m backpropagation_quickprop.m

function [D, Wh, Wo] = Backpropagation_Quickprop(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm and quickprop % Inputs:
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