|
142
|
Adaptive Fraud Detection
– Tom Fawcett, Foster, Foster Provost
- 1997
|
|
11
|
Distributed Data Mining: Scaling up and beyond
– Foster Provost
- 1999
|
|
40
|
Combining Data Mining and Machine Learning for Effective User Profiling
– Tom Fawcett, Foster Provost
- 1996
|
|
7
|
Rule-Space Search for Knowledge-Based Discovery
– Foster Provost, John M. Aronis, Bruce G. Buchanan
- 1999
|
|
46
|
Scaling Up: Distributed Machine Learning with Cooperation
– Foster John Provost, Daniel N. Hennessy
- 1996
|
|
17
|
The WoRLD: Knowledge Discovery from Multiple Distributed Databases
– John M. Aronis, Venkateswarlu Kolluri, Foster J. Provost, Bruce G. Buchanan
- 1997
|
|
15
|
A Survey of Methods for Scaling Up Inductive Learning Algorithms
– Foster J. Provost, Venkateswarlu Kolluri, Foster Provost
- 1997
|
|
15
|
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation
– John M. Aronis, Foster J. Provost
- 1997
|
|
47
|
Statistical Fraud Detection: A Review
– Richard J. Bolton, David J. Hand, David J. H
- 2002
|
|
118
|
Separate-and-conquer rule learning
– Johannes Fürnkranz
- 1999
|
|
2
|
Machine Learning as Massive Search
– Richard B. Segal
- 1997
|
|
74
|
A Survey of Methods for Scaling Up Inductive Algorithms
– Foster Provost, Venkateswarlu Kolluri
- 1999
|
|
32
|
Using Rule Sets to Maximize ROC Performance
– Tom Fawcett
- 2001
|
|
1
|
Further Pruning for Efficient Association Rule Discovery
– Songmao Zhang, Geoffrey I. Webb
|
|
3
|
Beyond Association Rules: Generalized Rule Discovery
– Geoffrey I. Webb, Songmao Zhang
|
|
31
|
On Detecting Differences Between Groups
– Geoffrey I. Webb, Shane M. Butler, Douglas Newlands
- 2003
|
|
37
|
Inductive Policy: The Pragmatics of Bias Selection
– Foster John Provost, Bruce G. Buchanan
- 1995
|
|
47
|
Efficient Search for Association Rules
– Geoffrey I. Webb
- 2000
|
|
2
|
Use of Domain Knowledge in Constructive Induction
– James P. Callan
- 1990
|
