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Dmitry Pavlov

Papers on DiSC'04


Document Preprocessing For Naive Bayes Classification and Clustering with Mixture of Multinomials

Document Preprocessing For Naive Bayes Classification and Clustering with Mixture of Multinomials

Publications


Note: Links lead to the DBLP on the Web.

Dmitry Pavlov

Dmitry Pavlov, Ramnath Balasubramanyan , Byron Dom , Shyam Kapur , Jignashu Parikh : Document preprocessing for naive Bayes classification and clustering with mixture of multinomials. KDD 2004 : 829-834

Dmitry Pavlov, Eren Manavoglu , David M. Pennock , C. Lee Giles : Collaborative Filtering with Maximum Entropy. IEEE Intelligent Systems 19 (6): 40-48 (2004)

Eren Manavoglu , Dmitry Pavlov, C. Lee Giles : Probabilistic User Behavior Models. ICDM 2003 : 203-210

Dmitry Pavlov: Sequence Modeling with Mixtures of Conditional Maximum Entropy Distributions. ICDM 2003 : 251-258

Dmitry Pavlov, Alexandrin Popescul , David M. Pennock , Lyle H. Ungar : Mixtures of Conditional Maximum Entropy Models. ICML 2003 : 584-591

Dmitry Pavlov, Padhraic Smyth : Approximate Query Answering by Model Averaging. SDM 2003

Dmitry Pavlov, Heikki Mannila , Padhraic Smyth : Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data. IEEE Trans. Knowl. Data Eng. 15 (6): 1409-1421 (2003)

Dmitry Pavlov, David M. Pennock : A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. NIPS 2002 : 1441-1448

Dmitry Pavlov, Padhraic Smyth : Probabilistic query models for transaction data. KDD 2001 : 164-173

Dmitry Pavlov, Jianchang Mao , Byron Dom : Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR 2000 : 2219-2222

Dmitry Pavlov, Darya Chudova , Padhraic Smyth : Towards scalable support vector machines using squashing. KDD 2000 : 295-299

Dmitry Pavlov, Heikki Mannila , Padhraic Smyth : Probabilistic Models for Query Approximation with Large Sparse Binary Data Sets. UAI 2000 : 465-472

Heikki Mannila , Dmitry Pavlov, Padhraic Smyth : Prediction with Local Patterns using Cross-Entropy. KDD 1999 : 357-361

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