Communications of the ACM
Recent advances in error rate estimation
Pattern Recognition Letters
Information Processing Letters
Automatic Pattern Recognition: A Study of the Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Estimation of Classifier Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Hi-index | 0.14 |
The computational learning approach shows that the concept descriptions acquired from examples are approximately correct with a degree of probability that grows with the size of the training sample. The same problem has also been widely investigated in the field of pattern recognition under a variety of problem settings. Some of the results obtained in both fields are surveyed and compared, and the limits of their applicability are analyzed. Moreover, new and tighter bounds for the growth function of some classes of Boolean formulas are presented.