artificial intelligence project 1 neural networks
DESCRIPTION
Artificial Intelligence Project 1 Neural Networks. Biointelligence Lab School of Computer Sci. & Eng. Seoul National University. Outline. Classification Problems Task 1 Estimate several statistics on Diabetes data set Task 2 - PowerPoint PPT PresentationTRANSCRIPT
Artificial IntelligenceArtificial IntelligenceProject 1Project 1
Neural NetworksNeural Networks
Biointelligence Lab
School of Computer Sci. & Eng.
Seoul National University
(C) 2000-2002 SNU CSE BioIntelligence Lab
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OutlineOutline
Classification Problems Task 1
Estimate several statistics on Diabetes data set
Task 2 Given unknown data set, find the performance as good as you
can get The test data is hidden.
(C) 2000-2002 SNU CSE BioIntelligence Lab
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Network Structure (1)Network Structure (1)
…
positive
negative
fpos(x) > fneg(x),→ x is postive
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Network Structure (2)Network Structure (2)
…
f (x) > thres,→ x is postive
Medical Diagnosis: DiabetesMedical Diagnosis: Diabetes
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Pima Indian DiabetesPima Indian Diabetes
Data (768) 8 Attributes
Number of times pregnant Plasma glucose concentration in an oral glucose tolerance test Diastolic blood pressure (mm/Hg) Triceps skin fold thickness (mm) 2-hour serum insulin (mu U/ml) Body mass index (kg/m2) Diabetes pedigree function Age (year)
Positive: 500, negative: 268
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Report (1/4)Report (1/4)
Number of Epochs
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Report (2/4)Report (2/4)
Number of Hidden Units At least, 10 runs for each setting
# Hidden
Units
Train Test
Average SD
Best Worst Average SD
Best Worst
Setting 1
Setting 2
Setting 3
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Report (3/4)Report (3/4)
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Report (4/4)Report (4/4)
Normalization method you applied. Other parameters setting
Learning rates Threshold value with which you predict an example as
positive. If f(x) > thres, you can say it is positive, otherwise negative.
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Challenge (1)Challenge (1)
Unknown Data Data for you: 3282 examples 16 dim-input vector labeled one of 5 classes 5 classes are: A,B, C, D, E
Test data 582 examples Labels are HIDDEN!
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Challenge (2)Challenge (2)
Data Train.txt : 3282 x 17 (16987 examples, 16 dim-input +
with last column as label) Test.txt: 582 x 16 (582 examples, 16 dim-input, labels a
re hidden)
Verify your NN at http://knight.snu.ac.kr/aiproj1/ai_nn.asp
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ABCDE
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examples#
classifiedcorrecly #score
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제출할 것제출할 것
최고 성능을 낸 제출자 명시 뉴럴넷 구조 최고 성능을 이끌어 내기 위해 자신이 시도한
내역 기술 자신의 최고 성능 (score) : 성능과 점수는
상관 관계가 작습니다 .
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ReferencesReferences
Source Codes Free softwares NN libraries (C, C++, JAVA, …) MATLAB Tool box Weka
Web sites http://www.cs.waikato.ac.nz/~ml/weka/
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Pay Attention!Pay Attention!
Due (October 14, 2003): until pm 11:59 Submission
Results obtained from your experiments Compress the data Via e-mail
Report: Hardcopy!! Used software and running environments Results for many experiments with various parameter settings Analysis and explanation about the results in your own way
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Optional ExperimentsOptional Experiments
Various learning rate Number of hidden layers Different k values Output encoding