Stephen D. Bay

发表

Research on the Knowledge Plane: Distributed Learning and Reasoning About Network Outages

Pat Langley, Stephen D. Bay, 2004 .

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

Stephen D. Bay, Lonnie Chrisman, Andrew Pohorille, 2004, J. Comput. Biol..

Robust Induction of Process Models from Time-Series Data

Kazumi Saito, Pat Langley, Stephen D. Bay, 2003, ICML.

Discovering and Describing Category Differences: What makes a discovered difference insightful?

Stephen D. Bay, Michael J. Pazzani, 2000 .

Massive search for detecting group differences

Stephen D. Bay, Michael J. Pazzani, 2001 .

Incorporating Biological Knowledge into Evaluation of Causal Regulatory Hypotheses

Pat Langley, Stephen D. Bay, Lonnie Chrisman, 2002, Pacific Symposium on Biocomputing.

Multivariate discretization of continuous variables for set mining

Stephen D. Bay, 2000, KDD '00.

Detecting change in categorical data: mining contrast sets

Stephen D. Bay, Michael J. Pazzani, M. Pazzani, 1999, KDD '99.

Characterizing Model Erros and Differences

Stephen D. Bay, Michael J. Pazzani, M. Pazzani, 2000, ICML.

Discovering Ecosystem Models from Time-Series Data

Kazumi Saito, Pat Langley, Stephen D. Bay, 2003, Discovery Science.

A framework for discovering anomalous regimes in multivariate time-series data with local models

Stephen D. Bay, 2004 .

Inducing Biological Models from Temporal Gene Expression Data

Kazumi Saito, Stephen D. Bay, Jeff Shrager, 2003, Discovery Science.

Nearest neighbor classification from multiple feature subsets

Stephen D. Bay, 1999, Intell. Data Anal..

Detecting Group Differences: Mining Contrast Sets

Stephen D. Bay, Michael J. Pazzani, M. Pazzani, 2001, Data Mining and Knowledge Discovery.

Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets

Stephen D. Bay, 1998, ICML.

The UCI KDD archive of large data sets for data mining research and experimentation

Stephen D. Bay, Padhraic Smyth, Michael J. Pazzani, 2000, SKDD.

Revising Qualitative Models of Gene Regulation

Kazumi Saito, Pat Langley, Stephen D. Bay, 2002, Discovery Science.

Mining distance-based outliers in near linear time with randomization and a simple pruning rule

Stephen D. Bay, Mark Schwabacher, M. Schwabacher, 2003, KDD '03.

The Independent Sign Bias: Gaining Insight from Multiple Linear Regression

Stephen D. Bay, Michael J. Pazzani, M. Pazzani, 2020, Proceedings of the Twenty First Annual Conference of the Cognitive Science Society.

Revising Engineering Models: Combining Computational Discovery with Knowledge

Pat Langley, Stephen D. Bay, Daniel G. Shapiro, 2002, ECML.

Multivariate Discretization for Set Mining

Stephen D. Bay, 2001, Knowledge and Information Systems.

Revising regulatory networks: from expression data to linear causal models

Pat Langley, Stephen D. Bay, Andrew Pohorille, 2002, J. Biomed. Informatics.

Inductive revision of quantitative process models

Stephen D. Bay, P. Langley, K. Arrigo, 2006 .

Characterizing Model Errors and Di erences

Stephen D. Bay, M. Pazzani, 2000 .

Characterizing Model Performance in the Feature Space

Stephen D. Bay, M. Pazzani, 2007 .

Discovering Ecosystem Models

Stephen D. Bay, P. Langley, K. Arrigo, 2007 .

Knowledge and Information Systems (2000) Xxx: ??{?? C Multivariate Discretization for Set Mining

Stephen D. Bay, 2000 .

Large Scale Detection of Irregularities in Accounting Data

Stephen D. Bay, David M. Steier, Stephen Bay, 2006, Sixth International Conference on Data Mining (ICDM'06).

Near Linear Time Detection of Distance-Based Outliers and Applications to Security

Stephen D. Bay, M. Schwabacher, 2009 .

EXAMINATION OF DIEL CHANGES IN GLOBAL TRANSCRIPT ACCUMULATION IN SYNECHOCYSTIS (CYANOBACTERIA) 1

Stephen D. Bay, Jeff Shrager, K. Arrigo, 2006 .

SNARE: a link analytic system for graph labeling and risk detection

Christos Faloutsos, Stephen D. Bay, David M. Steier, 2009, KDD.