Michael J. Pazzani
Michael J. Pazzani
Professor of Computer Science and Engineering, University of California, Riverside
Verified email at UCR.edu - Homepage
TitleCited byYear
On the optimality of the simple Bayesian classifier under zero-one loss
P Domingos, M Pazzani
Machine learning 29 (2-3), 103-130, 1997
Content-based recommendation systems
MJ Pazzani, D Billsus
The adaptive web, 325-341, 2007
A framework for collaborative, content-based and demographic filtering
MJ Pazzani
Artificial intelligence review 13 (5-6), 393-408, 1999
Learning and revising user profiles: The identification of interesting web sites
M Pazzani, D Billsus
Machine learning 27 (3), 313-331, 1997
Dimensionality reduction for fast similarity search in large time series databases
E Keogh, K Chakrabarti, M Pazzani, S Mehrotra
Knowledge and information Systems 3 (3), 263-286, 2001
Learning Collaborative Information Filters.
D Billsus, MJ Pazzani
Icml 98, 46-54, 1998
An online algorithm for segmenting time series
E Keogh, S Chu, D Hart, M Pazzani
Proceedings 2001 IEEE International Conference on Data Mining, 289-296, 2001
Syskill & Webert: Identifying interesting web sites
MJ Pazzani, J Muramatsu, D Billsus
AAAI/IAAI, Vol. 1, 54-61, 1996
Locally adaptive dimensionality reduction for indexing large time series databases
E Keogh, K Chakrabarti, M Pazzani, S Mehrotra
ACM Sigmod Record 30 (2), 151-162, 2001
Beyond independence: Conditions for the optimality of the simple bayesian classi er
P Domingos, M Pazzani
Proc. 13th Intl. Conf. Machine Learning, 105-112, 1996
Derivative dynamic time warping
EJ Keogh, MJ Pazzani
Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
User modeling for adaptive news access
D Billsus, MJ Pazzani
User modeling and user-adapted interaction 10 (2-3), 147-180, 2000
An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback.
EJ Keogh, MJ Pazzani
Kdd 98, 239-243, 1998
Segmenting time series: A survey and novel approach
E Keogh, S Chu, D Hart, M Pazzani
Data mining in time series databases, 1-21, 2004
A hybrid user model for news story classification
D Billsus, MJ Pazzani
UM99 User Modeling, 99-108, 1999
Machine learning for user modeling
GI Webb, MJ Pazzani, D Billsus
User modeling and user-adapted interaction 11 (1-2), 19-29, 2001
The utility of knowledge in inductive learning
M Pazzani, D Kibler
Machine learning 9 (1), 57-94, 1992
Detecting group differences: Mining contrast sets
SD Bay, MJ Pazzani
Data mining and knowledge discovery 5 (3), 213-246, 2001
Reducing misclassification costs
M Pazzani, C Merz, P Murphy, K Ali, T Hume, C Brunk
Machine Learning Proceedings 1994, 217-225, 1994
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