Machine Learning from Structured Objects

Stepp, Robert E.

doi:10.1016/b978-0-934613-41-5.50038-6

Cited by 12 publications

(8 citation statements)

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“…." Thus a scene (or its instance graph) satisfies ~ if it contains r distinct objects obj~ .... obJr such that for every i, 1 -< i <-s, if f/ = f~(xj) then objj satisfies f~ and if f~ = f~(xj, x~) then the ordered pair (obj,, objk) satisfies f. Note that the scene may also contain objects other than these r objects (see Stepp, 1987).…”

Section: Instances and Existential Conjunctive Conceptsmentioning

confidence: 99%

Learning conjunctive concepts in structural domains

Haussler

1989

Mach Learn

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We study the problem of learning conjunctive concepts from examples on structural domains like the blocks world. This class of concepts is formally defined, and it is shown that even for samples in which each example (positive or negative) is a two-object scene, it is NP-complete to determine if there is any concept in this class that is consistent with the sample. We demonstrate how this result affects the feasibility of Mitchell's version of space approach and how it shows that it is unlikely that this class of concepts is polynomially learnable from random examples alone in the PAC framework of Valiant. On the other hand, we show that for any fixed bound on the number of objects per scene, this class is polynomially learnable if, in addition to providing random examples, we allow the learning algorithm to make subset queries. In establishing this result, we calculate the capacity of the hypothesis space of conjunctive concepts in a structural domain and use a general theorem of Vapnik and Chervonenkis. This latter result can also be used to estimate a sample size sufficient for heuristic learning techniques that do not use queries.

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Section: Instances and Existential Conjunctive Conceptsmentioning

confidence: 99%

Learning conjunctive concepts in structural domains

Haussler

1989

Mach Learn

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“…They also apply to several interesting geometric languages. They do not hold for the existentially-quantified conjunctive predicate calculus statements that are sometimes used to represent structured objects (Stepp, 1987a;Larson, 1977).…”

Section: Resultsmentioning

confidence: 99%

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Pitt

Reinke

1988

Machine Learning

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Abstract. Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scientific data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters that have "good" descriptions in some language. In this paper we investigate the general properties that similarity metrics, objective functions, and concept description languages must have to guarantee that a (conceptual) clustering problem is polynomial-time solvable by a simple and widely used clustering technique, the agglomerative-hierarchical algorithm. We show that under fairly general conditions, the agglomerative-hierarchical method may be used to find an optimal solution in polynomial time.

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“…Second, numerical data from real-life applications are usually imprecise and incomplete, or noisy. Controlling noise to reduce uncertainty becomes another challenging issue in modeling activities (Stepp, 1987). Moreover, numerical data coexist with its unit; methods lacking facilities to cope with this coupling of the data and its unit may contain some inadequacy or produce an erroneous model.…”

Section: Learning Empirical Formulasmentioning

confidence: 99%

Learning design concepts using machine learning techniques

Maher

1994

AIEDAM

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The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.

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Machine Learning from Structured Objects

Cited by 12 publications

References 1 publication

Learning conjunctive concepts in structural domains

Learning conjunctive concepts in structural domains

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Learning design concepts using machine learning techniques

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