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ALT
1998
Springer
15 years 8 months ago
Predictive Learning Models for Concept Drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the con...
John Case, Sanjay Jain, Susanne Kaufmann, Arun Sha...
RSCTC
2004
Springer
134views Fuzzy Logic» more  RSCTC 2004»
15 years 9 months ago
Rough Set Methods in Approximation of Hierarchical Concepts
Abstract. Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concept...
Jan G. Bazan, Sinh Hoa Nguyen, Hung Son Nguyen, An...
ECCC
2002
74views more  ECCC 2002»
15 years 4 months ago
On the proper learning of axis parallel concepts
We study the proper learnability of axis-parallel concept classes in the PAC-learning and exactlearning models. These classes include union of boxes, DNF, decision trees and multi...
Nader H. Bshouty, Lynn Burroughs
FLAIRS
2001
15 years 5 months ago
Graph-Based Concept Learning
We introduce the graph-based relational concept learner SubdueCL. We start with a brief description of other graph-based learning systems: the Galois lattice, Conceptual Graphs, a...
Jesus A. Gonzalez, Lawrence B. Holder, Diane J. Co...
KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 8 months ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz