Teachability in computational learning

Shinohara, Ayumi; Miyano, Satoru

doi:10.1007/bf03037091

Cited by 101 publications

(92 citation statements)

References 6 publications

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“…There are some different settings [2], [4], [9]. In Goldman and Mathias's setting [2], at first, the teacher makes a teaching set T which is a set of examples for the learner.…”

Section: Teachabilitymentioning

confidence: 99%

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Teachability of a Subclass of Simple Deterministic Languages

Tajima

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe show teachability of a subclass of simple deterministic languages. The subclass we define is called stack uniform simple deterministic languages. Teachability is derived by showing the query learning algorithm for this language class. Our learning algorithm uses membership, equivalence and superset queries. Then, it terminates in polynomial time. It is already known that simple deterministic languages are polynomial time query learnable by context-free grammars. In contrast, our algorithm guesses a hypothesis by a stack uniform simple deterministic grammar, thus our result is strict teachability of the subclass of simple deterministic languages. In addition, we discuss parameters of the polynomial for teachability. The "thickness" is an important parameter for parsing and it should be one of parameters to evaluate the time complexity.

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“…There are some different settings [2], [4], [9]. In Goldman and Mathias's setting [2], at first, the teacher makes a teaching set T which is a set of examples for the learner.…”

Section: Teachabilitymentioning

confidence: 99%

“…In this problem, the learner finds out a correct hypothesis via a special set of examples. There are some different models of teachability [2], [4], [9]. In Goldman and Mathias's setting [2], the teacher makes a set of examples called teaching set which is helpful for the learner.…”

Section: Introductionmentioning

confidence: 99%

Teachability of a Subclass of Simple Deterministic Languages

Tajima

2013

IEICE Trans. Inf. & Syst.

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“…[5,6]); for threshold functions (cf. [32]); and for k-juntas and sparse GF 2 polynomials (cf. [26]).…”

Section: The Teaching Dimension Modelmentioning

confidence: 99%

“…They investigate how many mistakes a consistent learner can make in the worst case. In Shinohara and Miyano's [32] model the teacher produces a set of examples for the target concept such that it is the only consistent one in the concept class. The size of this set is the same as the worst case number of mistakes in the online model.…”

Section: Introductionmentioning

confidence: 99%

Recent Developments in Algorithmic Teaching

Balbach¹,

Zeugmann

2009

Language and Automata Theory and Applications

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Abstract. The present paper surveys recent developments in algorithmic teaching. First, the traditional teaching dimension model is recalled. Starting from the observation that the teaching dimension model sometimes leads to counterintuitive results, recently developed approaches are presented. Here, main emphasis is put on the following aspects derived from human teaching/learning behavior: the order in which examples are presented should matter; teaching should become harder when the memory size of the learners decreases; teaching should become easier if the learners provide feedback; and it should be possible to teach infinite concepts and/or finite and infinite concept classes. Recent developments in the algorithmic teaching achieving (some) of these aspects are presented and compared.

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“…The notion of characteristic sets leads in a natural way to the associated problem of teaching (Shinohara & Miyano, 1991;Goldman & Kearns, 1995;Jackson & Tomkins, 1992): a teacher's goal is to help the learner (or the identification algorithm), by providing a "good" set of examples. Following the general work on Freivalds et al, on good examples (1989), different models of teaching have in recent years been proposed (Anthony et al, 1992;Wiehagen, 1992;Mathias, 1995).…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Higuera

1997

Machine Learning

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Abstract. When concerned about efficient grammatical inference two issues are relevant: the first one is to determine the quality of the result, and the second is to try to use polynomial time and space. A typical idea to deal with the first point is to say that an algorithm performs well if it infers in the limit the correct language. The second point has led to debate about how to define polynomial time: the main definitions of polynomial inference have been proposed by Pitt and Angluin. We return in this paper to a definition proposed by Gold that requires a characteristic set of strings to exist for each grammar, and this set to be polynomial in the size of the grammar or automaton that is to be learned, where the size of the sample is the sum of the lengths of all strings it includes. The learning algorithm must also infer correctly as soon as the characteristic set is included in the data. We first show that this definition corresponds to a notion of teachability as defined by Goldman and Mathias. By adapting their teacher/learner model to grammatical inference we prove that languages given by context-free grammars, simple deterministic grammars, linear grammars and nondeterministic finite automata are not identifiable in the limit from polynomial time and data.

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Teachability in computational learning

Abstract: This paper considers computationai learning from the viewpoint of teaching. We isltroaace a notion of teachiability with which we establish a relatiionship between the learnabiiity and teachability. We also discuss the complexity issues of a teacher in relation to learning.

Cited by 101 publications

References 6 publications

Teachability of a Subclass of Simple Deterministic Languages

Teachability of a Subclass of Simple Deterministic Languages

Recent Developments in Algorithmic Teaching

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