Titlebook: Algorithmic Learning Theory - ALT ‘92; Third Workshop, ALT Shuji Doshita,Koichi Furukawa,Toyaki Nishida Conference proceedings 1993 Spring

細菌等

Inductive inference with bounded mind changes,nite tell-tale and a pair of .-bounded finite tell-tales of a language, and present a necessary and sufficient condition for a class to be inferable with bounded mind changes, when the equivalence of any two languages in the class is effectively decidable. We also show that the inferability of a cla

boisterous

Efficient inductive inference of primitive Prologs from positive data, is a proper subclass of one of linear Prologs that is known to be inferable from only positive data. In this paper, we discuss on the consistent and conservative polynomial update time inferability of the subclass. We give a consistent and conservative polynomial update time inference algorithm tha

遺忘

Monotonic language learning,tain constraints motivated by work in inductive reasoning. Previously, various notions of monotonicity have been defined in the context of language learning. These constraints require that the learner‘s guess monotonically ‘improves’ with regard to the target language. In this paper, the ideas from

Jacket

Prudence in vacillatory language identification (Extended abstract),h a learner M is learning a language . from positive data. Three different criteria for success of M on . have been investigated in formal language learning theory. If M converges to a single correct grammar for ., then the criterion of success is Gold‘s seminal notion of TxtEx-identification. If M

Hyperopia

譏笑

Planning with abstraction based on partial predicate mappings,lan, and instantiating the abstract plan to obtain a final plan at a concrete level. If an abstract plan cannot be instantiated to any plan at the concrete level, it is no use obtaining the final plan. To avoid such an instantiation failure, each abstract plan must be instantiated to one or more con

繁忙

goodwill

Diaphragm

An application of Bernstein polynomials in PAC model,l [2]. Let . be a Lipschitz bounded continuous function with constant L. We intend to approximate the function . with accuracy . and confidence .. By using Bernstein polynomials of degree .=[(3L/e).], we will construct a polynomial time algorithm which will learn an .-approximation to the function i

和音

Polynomial-time MAT learning of multilinear logic programs,algorithm is bounded by a polynomial in . and ., where . is the number of predicates in .., . is the number of non-linear clauses in .., and . is a parameter depending on counter-examples to equivalence queries. We also show multilinear programs with outputs are MAT learnable by extending the algorithm.