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Note: Links lead to the DBLP on the Web. Kamal Nigam Natalie S. Glance , Matthew Hurst , Kamal Nigam, Matthew Siegler , Robert Stockton , Takashi Tomokiyo : Deriving marketing intelligence from online discussion. KDD 2005 : 419-428 Natalie S. Glance , Matthew Hurst , Kamal Nigam, Matthew Siegler , Robert Stockton , Takashi Tomokiyo : Analyzing online discussion for marketing intelligence. WWW (Special interest tracks and posters) 2005 : 1172-1173 Kamal Nigam, Rayid Ghani : Analyzing the Effectiveness and Applicability of Co-training. CIKM 2000 : 86-93 Andrew McCallum , Kamal Nigam, Lyle H. Ungar : Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000 : 169-178 Mark Craven , Dan DiPasquo , Dayne Freitag , Andrew McCallum , Tom M. Mitchell , Kamal Nigam, Seán Slattery : Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118 (1-2): 69-113 (2000) Andrew McCallum , Kamal Nigam, Jason Rennie , Kristie Seymore : Automating the Construction of Internet Portals with Machine Learning. Inf. Retr. 3 (2): 127-163 (2000) Kamal Nigam, Andrew McCallum , Sebastian Thrun , Tom M. Mitchell : Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39 (2/3): 103-134 (2000) Andrew McCallum , Kamal Nigam, Jason Rennie , Kristie Seymore : A Machine Learning Approach to Building Domain-Specific Search Engines. IJCAI 1999 : 662-667 Mark Craven , Dan DiPasquo , Dayne Freitag , Andrew McCallum , Tom M. Mitchell , Kamal Nigam, Seán Slattery : Learning to Extract Symbolic Knowledge from the World Wide Web. AAAI/IAAI 1998 : 509-516 Kamal Nigam, Andrew McCallum , Sebastian Thrun , Tom M. Mitchell : Learning to Classify Text from Labeled and Unlabeled Documents. AAAI/IAAI 1998 : 792-799 Mark Craven , Seán Slattery , Kamal Nigam: First-Order Learning for Web Mining. ECML 1998 : 250-255 Andrew McCallum , Kamal Nigam: Employing EM and Pool-Based Active Learning for Text Classification. ICML 1998 : 350-358 1 [ 2 ] [ 4 ] [ 8 ] 2 [ 4 ] [ 8 ] 3 [ 4 ] [ 8 ] 4 [ 10 ] 5 [ 11 ] [ 12 ] 6 [ 11 ] [ 12 ] 7 [ 1 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] 8 [ 3 ] [ 4 ] [ 6 ] [ 8 ] 9 [ 5 ] [ 7 ] 10 [ 5 ] [ 7 ] 11 [ 11 ] [ 12 ] 12 [ 2 ] [ 4 ] [ 8 ] 13 [ 11 ] [ 12 ] 14 [ 3 ] [ 6 ] 15 [ 11 ] [ 12 ] 16 [ 9 ]  ©2006 Association for Computing Machinery |
