Overfitting and undercomputing in machine learning

suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough searches in learning decision trees and rule sets. Ironically, when these algorithms were tested on real datasets, it was found that their performance was often worse than simple gradient descent or greedy search [3, 5]. In short: it appears to be better not to optimize! One of the other important trends in machine learning research has been the establishment and nurturing of connections between various previously-disparate fields including computational learning theory, connectionist learning, symbolic learning, and statistics. The connection to statistics was crucial in resolving this paradox. The key problem arises from the structure of the machine learning task. A learning algorithm is trained on a set of training data, but then it is applied to make

# Machine Learning and Algorithms # Computer Science # Machine Learning and Data Classification # Stochastic Gradient Optimization Techniques

Thomas G. Dietterich (1995). "Overfitting and undercomputing in machine learning". ACM Computing Surveys. https://doi.org/10.1145/212094.212114