Shift of Bias as Non-Monotonic Reasoning

Grosof, Benjamin N.; Russell, Stuart

doi:10.1007/978-1-4613-1641-1_3

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Flexibility requires an explicit representation of the control level that determine what are the suitable learning operations to apply to a given problem. Eliciting the control of the learning process referred to as declarative bias has always been a fundamental issue in ML because it strongly a ects the complexity of the learning process and the learning results Mitchell, 1991], Utgo , 1986], Grosof and Russell, 1990]. Three of the works presented at the workshop also demonstrate the utility of a bias language as a concise and powerful way for the user to explicitly parameterize ML systems instead of tuning low level knowledge such as examples and domain theory.…”

Section: Bias Languagementioning

confidence: 99%

Integration of Machine Learning and Knowledge Acquisition

Nédellec

1995

The Knowledge Engineering Review

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“Integration of Machine Learning and Knowledge Acquisition” may be a surprising title for an ECAI-94 workshop, since most machine learning (ML) systems are intended for knowledge acquisition (KA). So what seems problematic about integrating ML and KA? The answer lies in the difference between the approaches developed by what is referred to as ML and KA research. Apart from sonic major exceptions, such as learning apprentice tools (Mitchell et al., 1989), or libraries like the Machine Learning Toolbox (MLT Consortium, 1993), most ML algorithms have been described without any characterization in terms of real application needs, in terms of what they could be effectively useful for. Although ML methods have been applied to “real world” problems few general and reusable conclusions have been drawn from these knowledge acquisition experiments. As ML techniques become more and more sophisticated and able to produce various forms of knowledge, the number of possible applications grows. ML methods tend then to be more precisely specified in terms of the domain knowledge initially required, the control knowledge to be set and the nature of the system output (MLT Consortium, 1993; Kodratoff et al., 1994).

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Section: Bias Languagementioning

confidence: 99%

Integration of Machine Learning and Knowledge Acquisition

Nédellec

1995

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Of (4)(5)(6)(7)(8)(9)(10)(11)(12), only (5) is interesting and is expanded; the rest are set aside. (17) is uninteresting. (15) is not only interesting but is also operational, so it is added to the set of sets.…”

Section: Detailed Examplementioning

confidence: 99%

“…This section is a close recapitulation of parts of [17]. Next, we 'lift' KBDL into KBIL by moving to non-monotonic logic.…”

Section: Going Non-monotonicmentioning

confidence: 99%