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Intro ] full-2 ] medium-1000 ]
medium-10000 ] medium-2000 ] medium-3000 ]
medium-4000 ] medium-5000 ] medium-6000 ]
medium-6000a-zero ] medium-6000a ] medium-6000b ]
medium-7000 ] medium-8000 ] medium-9000 ]
medium.2-1000 ] medium.2-10000 ] medium.2-2000 ]
medium.2-3000 ] medium.2-4000 ] medium.2-5000 ]
medium.2-6000 ] medium.2-7000 ] medium.2-8000 ]
medium.2-9000 ] optimal-1 ] optimal-1a ]
optimal-2 ] optimal-3 ] optimal-4 ]
optimal-5 ] optimal-5a ] optimal-6 ]
param-learn ] param-momentum ] parameter-matrix ]
paramselection-hidden ] short-1000 ] short-2000 ]
short-3000 ] short-4000 ] short-5000 ]
short-6000 ] short.2-1000 ] short.2-2000 ]
short.2-3000 ] short.2-4000 ] short.2-5000 ]
short.2-6000 ] simple-10000 ] simple-100000 ]
[ simple-2000 ] simple-5000 ]

Up: Report ]

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.32048888500310957	0.03934822434544061	age
Hidden Weights:
	-0.743397141457063	0.8293823345580009
	0.20298606102141492	-0.21788696895113957
		>50K		<=50K

Error sum for iteration 100 = 59.25125929109509

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.034204533626444385	-0.2789663502963451	age
Hidden Weights:
	-0.8325842400166106	0.9170487962445274
	0.14251523337474545	-0.18978799901672758
		>50K		<=50K

Error sum for iteration 201 = 60.446700449418294

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.04065576030064921	-0.3083022554022436	age
Hidden Weights:
	-1.098812457328804	1.1791256834185373
	0.13792039437924947	-0.1852364783526837
		>50K		<=50K

Error sum for iteration 302 = 61.88792155197696

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.05314052476022916	-0.32507597126935384	age
Hidden Weights:
	-1.404808442999115	1.4781171230354293
	0.13521804319536884	-0.18256873293259904
		>50K		<=50K

Error sum for iteration 403 = 63.177179801146494

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.07244985041060645	-0.33572998633577583	age
Hidden Weights:
	-1.7394330264807945	1.801944329584247
	0.13347452898483567	-0.1808560052800573
		>50K		<=50K

Error sum for iteration 504 = 63.72704065796363

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.09672877345143953	-0.3424051069283436	age
Hidden Weights:
	-2.063459635683654	2.1129078851839433
	0.13237756234980577	-0.17978416689858032
		>50K		<=50K

Error sum for iteration 605 = 63.574205578551734

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.12134909891477626	-0.34647096887626033	age
Hidden Weights:
	-2.3360462866945353	2.3733816155224794
	0.13171522726618096	-0.17913933019210337
		>50K		<=50K

Error sum for iteration 706 = 63.1848098708934

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.14198984253287583	-0.34897039343442676	age
Hidden Weights:
	-2.547721625223744	2.575652024848619
	0.13131675714923732	-0.17875185457925574
		>50K		<=50K

Error sum for iteration 807 = 62.82518766306001

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.15790862953956342	-0.3505582663187754	age
Hidden Weights:
	-2.706443155664248	2.727582498686221
	0.1310720117904879	-0.17851385930331293
		>50K		<=50K

Error sum for iteration 908 = 62.55095050134649

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.17094230904366223	-0.3516076382527103	age
Hidden Weights:
	-2.8234205582884084	2.8397135623695036
	0.1309176814006914	-0.17836376750944577
		>50K		<=50K

Error sum for iteration 1009 = 62.33328118147873

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.18217344791040838	-0.3523263999957532	age
Hidden Weights:
	-2.90927208239162	2.922055332686004
	0.13081849858913652	-0.17826735124141657
		>50K		<=50K

Error sum for iteration 1110 = 62.15460666609947

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.1920205193329269	-0.3528317060678578	age
Hidden Weights:
	-2.9722664745390532	2.982449815048388
	0.13075466554540752	-0.17820539627026655
		>50K		<=50K

Error sum for iteration 1211 = 62.01990288800556

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.20071101099634578	-0.3531921568516267	age
Hidden Weights:
	-3.0182990575244193	3.026512621766249
	0.13071464749811565	-0.1781666979369542
		>50K		<=50K

Error sum for iteration 1312 = 61.91414562012878

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.20841797005219972	-0.353449774167204	age
Hidden Weights:
	-3.0514483422929803	3.0581396231713724
	0.13069141498645703	-0.1781444200039638
		>50K		<=50K

Error sum for iteration 1413 = 61.81845859443648

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.21528746946440985	-0.35363144625831994	age
Hidden Weights:
	-3.0745258083487714	3.0800207072844485
	0.13068049211887153	-0.17813420435687835
		>50K		<=50K

Error sum for iteration 1514 = 61.73620554827852

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.22144307610174221	-0.3537550944398007	age
Hidden Weights:
	-3.0894965037834945	3.0940378212515642
	0.13067890971542365	-0.17813315646809846
		>50K		<=50K

Error sum for iteration 1615 = 61.66473055012075

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.2269878385337108	-0.3538331146897827	age
Hidden Weights:
	-3.097768368481578	3.101540599393318
	0.13068462373560585	-0.178139281296009
		>50K		<=50K

Error sum for iteration 1716 = 61.60167704817444

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.2320069714476083	-0.35387436490524815	age
Hidden Weights:
	-3.1003800582928718	3.1035258038846565
	0.13069618069831082	-0.17815115850660362
		>50K		<=50K

Error sum for iteration 1817 = 61.54694633964177

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.23657085786517448	-0.35388535409423605	age
Hidden Weights:
	-3.0981202856220738	3.1007514102983196
	0.1307125180061718	-0.17816774851493528
		>50K		<=50K

Error sum for iteration 1918 = 61.49817647190738

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.2407378433184977	-0.3538709807858507	age
Hidden Weights:
	-3.0916040343668025	3.093809435582207
	0.13073284021631035	-0.17818827221530464
		>50K		<=50K


Options: -I 2000 

Back Propagation Neural Network:
Numer Input Nodes: 1
Number Hidden Nodes: 2
Number Output Nodes: 2
	-I	2000
	-E	0.01
	-S	1623219896
	-H	2
	-M	0.01
Input Weights:
	-0.24382601524793351	-0.35384369652668995	age
Hidden Weights:
	-3.0836370875261694	3.0855533891692377
	0.13075160259694724	-0.17820716727720215
		>50K		<=50K



=== Error on training data ===

Correctly Classified Instances         387               77.4    %
Incorrectly Classified Instances       113               22.6    %
Mean absolute error                      0.3416
Root mean squared error                  0.401 
Relative absolute error                 97.4899 %
Root relative squared error             95.8896 %
Total Number of Instances              500     


=== Confusion Matrix ===

   a   b   <-- classified as
   0 113 |   a = >50K
   0 387 |   b = <=50K


=== Error on test data ===

Correctly Classified Instances         385               76.8463 %
Incorrectly Classified Instances       116               23.1537 %
Mean absolute error                      0.3376
Root mean squared error                  0.4024
Relative absolute error                 95.5207 %
Root relative squared error             95.3955 %
Total Number of Instances              501     


=== Confusion Matrix ===

   a   b   <-- classified as
   0 116 |   a = >50K
   0 385 |   b = <=50K


by: Keith A. Pray
Last Modified: July 4, 2004 9:05 AM
© 2004 - 1975 Keith A. Pray.
All rights reserved.

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