Machine Learning of Inductive Bias
This book is based on the author's Ph.D. dissertation. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.
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A:FR AFR1 algorithm applied APPLY-OP-FAILS appropriate bias Artificial Intelligence assimilation better bias BINDINGS BOUND-VALUE CADDR CADR CDDR CDR P1CP2C compute concept description language concept learner COND ATOM COND EQ consistent description consistent hypotheses Constraint Back-Propagation procedure defined DEFPROP DERIVATIVE-FR describable disjunctive description domain EURISKO example FIND-BINDINGS FLAGS MG-CLEAN MS-CLEAN FLAGS P1CP2C COMPARISONS formalism FR1 FR2 function grammar rule heuristic IEXPR inductive bias INST INT1 integers intersection learning program Least Disjunction procedure LEX's LHS CAR LIST CONS LMAP LOCAL-STACK LVAR Machine Learning MAPCAR F:L MATCH MEMQ MG MS FLAGS negative instances NO-AMB-DERS NO-INTO-MAPS NON-NUM-OPERANDS numbers NVAL OPERANDS operator sequence ordered pair P1 P2 FLAGS PLIS POS-PATS positive instances propagation recognition predicate restricted hypothesis space RMAP RTA Method sentential form SETQ shifting bias specific STABB subset target concept TCDR TEMP TEMP2 TMPPLIS training instances trig version space