Boolean Feature Discovery in Empirical Learning
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Autonomous Learning from the Environment
Autonomous Learning from the Environment
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
Machine Learning
Learning from the environment based on percepts and actions
Learning from the environment based on percepts and actions
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This paper describes the integration of a learning mechanism called complementary discrimination learning with a knowledge representation schema called decision lists. There are two main results of such an integration. One is an efficient representation for complementary concepts that is crucial for complementary discrimination style learning. The other is the first behaviorally incremental algorithm, called CDL2, for learning decision lists. Theoretical analysis and experiments in several domains have shown that CDL2 is more efficient than many existing symbolic or neural network learning algorithms, and can learn multiple concepts from noisy and inconsistent data.