Towards Scaling Up Machine Learning : A Case Study

Machine learning has proven itself in the small, although theoretical, al-gorithmic and implementational advances at the foundational level will continue to improve the basic building blocks in the eld. Empirical induction methods have been developed Michalski et al. at the symbolic level and tested on standard (albeit small) test suites, 1 and occasionally they have been used externally, as in the case of decision are also well developed. Analytical generalization methods are proving successful in improving performance in a variety of planning and other reasoning tasks. These methods in-The time has come to address machine learning in the large, including both for inductive concept acquisition Catlett, 1991, Quin-lan, 1987] and analytic performance improvements. In this chapter, 1. The University of California at Irvine maintains informally a varied set of training and test data for inductive generalization deposited and accessible by researchers in machine learning.