Average case analysis of conjunctive learning algorithms
Proceedings of the seventh international conference (1990) on Machine learning
An introduction to randomized algorithms
Discrete Applied Mathematics - Special volume: combinatorics and theoretical computer science
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A critique of the valiant model
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Average-case analysis of a nearest neighbor algorthim
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
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In machine learning, it is important to reduce computational time to analyze learning algorithms. Some researchers have attempted to understand learning algorithms by experimenting them on a variety of domains. Others have presented theoretical methods of learning algorithm by using approximately mathematical model. The mathematical model has some deficiency that, if the model is too simplified, it may lose the essential behavior of the original algorithm. Furthermore, experimental analyses are based only on informal analyses of the learning task, whereas theoretical analyses address the worst case. Therefore, the results of theoretical analyses are quite different from empirical results. In our framework, called random case analysis, we adopt the idea of randomized algorithms. By using random case analysis, it can predict various aspects of learning algorithm's behavior, and require less computational time than the other theoretical analyses. Furthermore, we can easily apply our framework to practical learning algorithms.