代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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www.eeworm.com/read/175689/5343329

m detreeexp32.m

global data textdata numobs global x y j quadclass tree global dtnum dtnode dtclass [x,y] = meshgrid(1.3:.02:2,30:2:100); x = x(:); y = y(:); j = classify([x y],data(:,1:2),textdata); gsc
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www.eeworm.com/read/175689/5343332

m detreeexp52.m

global data textdata numobs global x y j quadclass tree global dtnum dtnode dtclass [x,y] = meshgrid(0:3:100,0:3:100); x = x(:); y = y(:); j = classify([x y],data(:,1:2),textdata); gscatte
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www.eeworm.com/read/429426/1949349

entries

D/Associate//// D/Classify//// D/Data//// D/Evaluate//// D/Other//// D/Visualize//// D/icons//// /OWOptions.py/1.2/Wed May 7 14:07:03 2003//Tstable /ColorPalette.py/1.3/Mon Oct 24 11:03:40 20
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www.eeworm.com/read/429426/1949352

old entries.old

D/Associate//// D/Classify//// D/Data//// D/Evaluate//// D/Other//// D/Visualize//// D/icons//// /OWOptions.py/1.2/Wed May 7 14:07:03 2003//Tstable /OWToolbars.py/1.5/Thu Nov 25 12:24:17 2004
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www.eeworm.com/read/428780/1954003

m detreeexp32.m

global data textdata numobs global x y j quadclass tree global dtnum dtnode dtclass [x,y] = meshgrid(1.3:.02:2,30:2:100); x = x(:); y = y(:); j = classify([x y],data(:,1:2),textdata); gsc
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www.eeworm.com/read/428780/1954006

m detreeexp52.m

global data textdata numobs global x y j quadclass tree global dtnum dtnode dtclass [x,y] = meshgrid(0:3:100,0:3:100); x = x(:); y = y(:); j = classify([x y],data(:,1:2),textdata); gscatte
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www.eeworm.com/read/386597/2570106

m c4_5.m

function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node) % Classify using Quinlan's C4.5 algorithm % Inputs: % training_patterns - Train patterns % training_target
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www.eeworm.com/read/356870/3036358

c netmask.c

/* netmask.c: * * Classify an IP address: */ #include #include #include #include #include #include int main(int argc,ch
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www.eeworm.com/read/474600/6813421

m c4_5.m

function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node) % Classify using Quinlan's C4.5 algorithm % Inputs: % training_patterns - Train patterns % training_target
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www.eeworm.com/read/474600/6813450

asv c4_5.asv

function test_targets = C4_5(train_patterns, train_targets, test_patterns, inc_node) % Classify using Quinlan's C4.5 algorithm % Inputs: % training_patterns - Train patterns % training_target
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