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Jeff Roth

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Faculty - Target = Berkley. Training = Cornell, Texas, Washington. Source = Michigan ... Training = Berkley, Texas, Washington. Source = Michigan ... – PowerPoint PPT presentation

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Title: Jeff Roth


1
Jeff Roth
  • Project 3

2
Basic Results
  • Course - Target Reed
  • Training Rice, uwm, Washington
  • Source wsu
  • Naïve Bayes 7 / 12 correct, 6 / 16 FP
  • Name Classifier 12 / 15 correct, 0 / 19 FP

Course - Target Rice Training Reed, uwm,
Washington Source wsu Naïve Bayes 7 / 10
correct, 5 / 16 FP Name Classifier 12 / 13
correct, 0 / 19 FP
Faculty - Target Berkley Training Cornell,
Texas, Washington Source Michigan Naïve Bayes
6 / 10 correct, 3 / 10 FP Name Classifier 14 /
14 correct, 0 / 14 FP
Faculty - Target Cornell Training Berkley,
Texas, Washington Source Michigan Naïve Bayes
5 / 10 correct, 3 / 10 FP Name Classifier 14 /
14 correct, 0 / 14 FP
3
Improved Naïve Bayes
  • Course - Target Reed
  • Training Rice, uwm, Washington
  • Source wsu
  • Naïve Bayes 7 / 12 correct, 7 / 16 FP

Course - Target Rice Training Reed, uwm,
Washington Source wsu Naïve Bayes 7 / 10
correct, 5 / 16 FP
Faculty - Target Berkley Training Cornell,
Texas, Washington Source Michigan Naïve Bayes
6 / 10 correct, 3 / 10 FP
Faculty - Target Cornell Training Berkley,
Texas, Washington Source Michigan Naïve Bayes
5 / 10 correct, 3 / 10 FP
Improvements 1. Classification argmax
(Log(P(vj) S log(P(ai vj))) - included in
basic 2. If a word in classification doc has no
match, classification 1 / (2 vocabulary)
- no help 3. Divide by number of words in test
doc and find global max - scratched
4
Combination
  • Course - Target Reed
  • Training Rice, uwm, Washington
  • Source wsu
  • Name Classifier 13 / 15 correct, 0 / 19 FP

Course - Target Rice Training Reed, uwm,
Washington Source wsu Name Classifier 12 / 13
correct, 0 / 19 FP
Faculty - Target Berkley Training Cornell,
Texas, Washington Source Michigan Name
Classifier 14 / 14 correct, 0 / 14 FP
Faculty - Target Cornell Training Berkley,
Texas, Washington Source Michigan Name
Classifier 14 / 14 correct, 0 / 14 FP
Combination algorithm 1. Match source to target
if both Naïve Bayes and name matcher agreed 2.
Match remaining unmatched target elements to
source by name matcher 3. Match any remaining
unmatched target elements to source by Naïve Bayes
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