- ~43.4.1 MABEL: A Be$inner's Progran~ning Languase. P.R. King, G. Cormack, G. Dueck, R. Jung, G. Kusner, J. Melnyk
- Spam Filter Evaluation with Imprecise Ground Truth Gordon V. Cormack
- Using old Spam and Ham Samples to Train Email Filters Jose-Marcio Martins da Cruz
- Autonomous Personal Filtering Improves Global Spam Filter Performance
- Spam Filtering for Short Messages Gordon V. Cormack
- University of Waterloo Participation in the TREC 2007 Spam Gordon V. Cormack
- Data Compression Models Prediction and Classification
- On the Relative Age of Spam and Ham Training Samples for Email Filtering
- Validity and Power of t-Test for Comparing MAP and GMAP Gordon V. Cormack and Thomas R. Lynam
- On-line Supervised Spam Filter Evaluation GORDON V. CORMACK and THOMAS R. LYNAM
- Cormack DMC Spam Filtering 3 April, 2006 Dynamic Markov Coding
- Genre-based Decomposition of Email Class Noise Aleksander Kolcz
- Novelty and Diversity in Information Retrieval Evaluation Charles L. A. Clarke Maheedhar Kolla Gordon V. Cormack Olga Vechtomova
- Harnessing Unlabeled Examples through Iterative Application of Dynamic Markov
- MultiText Legal Experiments at TREC 2007 Stefan Bttcher, Charles L. A. Clarke, Gordon V. Cormack, Thomas R. Lynam
- Semi-supervised Spam Filtering: Does it Work? Mona Mojdeh and Gordon V. Cormack
- On-line Spam Filter Fusion Thomas R. Lynam and Gordon V. Cormack
- Going Mini: Extreme Lightweight Spam Filters Google, Inc.
- Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods
- Power and Bias of Subset Pooling Strategies Gordon V. Cormack and Thomas R. Lynam
- A Larger Decidable Semiunification Problem Brad Lushman
- TREC 2007 Spam Track Overview Gordon Cormack
- Content-based Web Spam Detection Gordon V. Cormack
- Objective Scoring for Computing Competition Tasks Graeme Kemkes, Troy Vasiga, and Gordon Cormack
