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Automatic Document Categorization using Support Vector Machines Prashanth Kumar Muthoju [email_address] Advisor: Dr. Zubair
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object]
Introduction .. Cont.d ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction .. Cont.d ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction .. Cont.d ,[object Object],[object Object],[object Object],[object Object]
Problem ,[object Object],[object Object]
Towards the solution .. ,[object Object],[object Object],[object Object],[object Object],[object Object]
Towards the solution .. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Towards the solution .. ,[object Object],[object Object],[object Object]
Towards the solution .. ,[object Object],[object Object]
Solution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Solution ,[object Object],[object Object],[object Object],Collection Model by Dr. Zeil Field/Group K Field/Group K Download Documents ( PDF ) Convert PDF to Text Model Documents Using TF and IDF Positive Training Set for Negative Training Set for Field/Group K SVM For
Solution ,[object Object],Field/Group 1 Field/Group K Field/Group N Trained SVM For Trained SVM For Trained SVM For Input Test Document ( PDF ) Convert PDF to Text Model Documents Using TF and IDF Estimate in the range 0 to 1 indicating how likely the Field/Group K maps to the test document .
Improving the results ,[object Object],[object Object]
Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results 140200 120200 201300 220200 250400 140200 13 2 1 2 2 120200 1 16 0 3 0 201300 0 5 13 2 0 220200 1 0 2 17 0 250400 0 0 1 0 19
Results ..Cont.d Category Precession Recall 140200 0.87 0.65 120200 0.70 0.80 201300 0.76 0.65 220200 0.71 0.85 250400 0.90 0.95
Future Work ,[object Object],In flat model, we consider each field/group independent. In Hierarchical model, we consider all files under the branch as positive files for training 150000 150300 150600 150301 150302 150601 150602
Future Work ,[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object]

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pmuthoju_presentation.ppt

  • 1. Automatic Document Categorization using Support Vector Machines Prashanth Kumar Muthoju [email_address] Advisor: Dr. Zubair
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  • 18. Results 140200 120200 201300 220200 250400 140200 13 2 1 2 2 120200 1 16 0 3 0 201300 0 5 13 2 0 220200 1 0 2 17 0 250400 0 0 1 0 19
  • 19. Results ..Cont.d Category Precession Recall 140200 0.87 0.65 120200 0.70 0.80 201300 0.76 0.65 220200 0.71 0.85 250400 0.90 0.95
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