Algorithms that learn sets of rules describing a concept from its examples have been widely studied in machine learning and have been applied to problems in medicine, molecular biology, planning and linguistics. Many of these algorithms used a separate-and-conquer strategy, repeatedly searching for rules that explain different parts of the example set. When examples are scarce, however, it is difficult for these algorithms to evaluate the relative quality of two or more rules which fit the examples equally well. This dissertation proposes, implements and examines a general technique for modifying rule evaluation in order to improve learning performance in these situations. This approach, called Description-based Evaluation Function Transfer (DEFT), adjusts the way rules are evaluated on a target concept by taking into account the performance of similar rules on a related support task that is supplied by a domain expert. Central to this approach is a novel theory of task similarity that is defined in terms of syntactic properties of rules, called descriptions, which define what it means for rules to be similar. Each description is associated with a prior distribution over classification probabilities derived from the support examples and a rule's evaluation on a target task is combined with the relevant prior using Bayes' rule. Given some natural conditions regarding the similarity of the target and support task, it is shown that modifying rule evaluation in this way is guaranteed to improve estimates of the true classification probabilities. Algorithms to efficiently implement Deft are described, analysed and used to measure the effect these improvements have on the quality of induced theories. Empirical studies of this implementation were carried out on two artificial and two real-world domains. The results show that the inductive transfer of evaluation bias based on rule similarity is an effective and practical way to improve learning when training examples are limited.

Dissertation (Ph.D.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographic references (p.176-181).

Thesis (M.Sc. (Computer Science)) -- Universiteit van Pretoria, 2007. / Includes bibliographical references.

Thesis (Ph.D.) - University of St Andrews, June 2008.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1983. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 61-65).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.S. in Nuclear Engineering)--Naval Postgraduate School, January 1963. / Thesis Advisor(s): Gerba, Alex. "January 1963." Description based on title screen as viewed on June 2, 2010. DTIC Descriptor(s): Research Reactors, Control Systems, Neutron Detectors, Neutron Flux, Reaction Kinetics, Equations, Simulation, Production Reactors, Test Reactors, Computer Programming, Analog Computers, Digital Computers, Nuclear Reactions, Performance (Engineering), Specifications, Neutron Activation. DTIC Identifier(s): AGN-201 Reactors. Includes bibliographical references (p. 54-55). Also available in print.

Thesis (M.S. in Physics)--Naval Postgraduate School, March 2010. / Thesis Advisor(s): Rodeback, G. W. "January 1962." Description based on title screen as viewed on June 2, 2010. DTIC Descriptor(s): Research Reactors, Hafnium, Half Life, Pneumatic Equipment, Measuring Instruments, Reactor Cores, Shielding, Detection, Gamma Emission, Computer Programming, Radioactive Isotopes, Radioactive Decay, Mixtures, Scintillation Counters, Isotopes, Least Squares Method. DTIC Identifier(s): AGN-201 Reactors, Fortran. Includes bibliographical references (p. 25). Also available in print.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, Dec. 2004. / Thesis Advisor(s): Wen, Su ; Gibson, John. "December 2004." Includes bibliographical references (p. 63-64). Also available in print.

Thesis (Ph.D.)--University of Waikato, 2006. / Includes bibliographical references (p. 157-170)