1. GammaWare Technology June 2002 Yiftach Ravid, VP R&D GammaSite Inc. [email_address]

2. Overview - The challenge - Taxonomies - Classification - Focused Crawler - Q&A - Categorization

4. Taxonomy

7. Taxonomy - Sample

9. Classification

13. Sample for Machine Learning DOGS CATS

14. Discriminating Features Q1 : Who is this person? Q2 : What are the most discriminating features?

16. Discriminating Features The “Margaret Thatcher effect”

18. Supervised Inductive Learning Example Training Test errors correct

19. Evaluating a Classifier Category Classifier

20. Recall and Precision Precision (P) = GY / (GY + GN) = 70 / (70+50) = 0.58 F-measure (F) = 2/(1/P + 1/R) = 2*GY/(GY+GN+GY+BY) = 2*70/(100+120) = 0.63 Recall (R) = GY / (GY + BY) = 70 / (70+30) = 0.70 Accuracy (A) = (GY+NN)/(GY+GN+BY+BN) = 220 / 300 = 0.73 Use a confusion matrix to count 300 180 120 Total Classified Bad Good True Label 200 100 150 30 50 70 Total No Yes

23. Getting Started

24. GammaWare Work Flow Check Seed Improve Classifiers Requirements Design the Taxonomy Seeding Process Train Classifiers Catalogue Documents Ready

32. Categorization

35. Focused Crawler

40. GammaWare API

41. Architecture - Basic Relational Database Customer Client GammaWare API CORBA GammaWare Proxy File System Relational Database GammaWare Software Proxy Client ODBC CORBA GW File System Document Management Web File System Notes Domino Outlook

43. Q & A