Reflecting inductive inference machines and its improvement by therapy

Grieser, Gunter

doi:10.1007/3-540-61863-5_56

Cited by 8 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability of reflec tion has been introduced into learning theory around 1994 ... 96 by lantke (14), lantke (15), Grieser (9), and Grieser (10). This is carried over to applications as in lantke (17).…”

Section: The General Perspectivementioning

confidence: 99%

Advantageous next generation adaptivity through reflection

Jantke

2010

IET International Conference on Frontier Computing. Theory, Technologies and Applications

View full text Add to dashboard Cite

Section: The General Perspectivementioning

confidence: 99%

Advantageous next generation adaptivity through reflection

Jantke

2010

IET International Conference on Frontier Computing. Theory, Technologies and Applications

View full text Add to dashboard Cite

“…Learning functions with refutation was considered by Miyahara [29] for indexed classes of primitive recursive functions. For arbitrary classes of arbitrary recursive functions, an alternative approach has been developed and studied by Jantke [20] and Grieser [17]. In a sense, their approach is orthogonal to ours.…”

Section: Introductionmentioning

confidence: 99%

On learning of functions refutably

Jain

Kinber

Wiehagen

et al. 2003

Theoretical Computer Science

View full text Add to dashboard Cite

“…Definitions 18 and 19 Since the pioneering paper [29] learning with refutation has attracted much attention (cf. [30,24,16,28,19,15]). …”

Section: Introductionmentioning

confidence: 99%

Learning Recursive Functions Refutably

Jain

Kinber²,

Wiehagen

et al. 2001

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Learning of recursive functions refutably means that for every recursive function, the learning machine has either to learn this function or to refute it, i.e., to signal that it is not able to learn it. Three modi of making precise the notion of refuting are considered. We show that the corresponding types of learning refutably are of strictly increasing power, where already the most stringent of them turns out to be of remarkable topological and algorithmical richness. All these types are closed under union, though in different strengths. Also, these types are shown to be different with respect to their intrinsic complexity; two of them do not contain function classes that are "most difficult" to learn, while the third one does. Moreover, we present characterizations for these types of learning refutably. Some of these characterizations make clear where the refuting ability of the corresponding learning machines comes from and how it can be realized, in general. For learning with anomalies refutably, we show that several results from standard learning without refutation stand refutably. Then we derive hierarchies for refutable learning. Finally, we show that stricter refutability constraints cannot be traded for more liberal learning criteria.

show abstract

Reflecting inductive inference machines and its improvement by therapy

Cited by 8 publications

References 10 publications

Advantageous next generation adaptivity through reflection

Advantageous next generation adaptivity through reflection

On learning of functions refutably

Learning Recursive Functions Refutably

Contact Info

Product

Resources

About