Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning
arXiv:2610.02615 · doi:10.1016/B978-1-55860-200-7.50034-9
Abstract
Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning performance. For example, we would like to quantify the value of constructive induction, noise filtering, and background knowledge. This paper describes the effective dimension, a new learning measure that helps link problem properties to learning performance. Like the Vapnik-Chervonenkis (VC) dimension, the effective dimension is often in a simple linear relation with problem properties. Unlike the VC dimension, the effective dimension can be estimated empirically and makes average-case predictions. It is therefore more widely applicable to machine and human learning research. The measure is demonstrated on several learning systems including Backpropagation. Finally, the measure is used to precisely predict the benefit of using FRINGE, a feature construction system. The benefit is found to decrease as the complexity of the target concept increases.
12 pages, including a modern cover note and the unchanged 11-page author manuscript from 1991. Extended author version of a paper published in Machine Learning Proceedings 1991 (ICML 1991), pp. 153-157. Deposited in arXiv in 2026