Generalization behaviour of alkemic decision trees

K. S. Ng

doi:10.1007/11536314_15

Generalization behaviour of alkemic decision trees

K. S. Ng^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper is concerned with generalization issues for a decision tree learner for structured data called ALKEMY. Motivated by error bounds established in statistical learning theory, we study the VC dimensions of some predicate classes defined on sets and multisets - two data-modelling constructs used intensively in the knowledge representation formalism of ALKEMY - and from that obtain insights into the (worst-case) generalization behaviour of the learner. The VC dimension results and the techniques used to derive them may be of wider independent interest.

Original language	English
Pages (from-to)	246-263
Number of pages	18
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3625
DOIs	https://doi.org/10.1007/11536314_15
Publication status	Published - 2005
Event	15th International Conference on Inductive Logic Programming, ILP 2005 - Bonn, Germany Duration: 10 Aug 2005 → 13 Aug 2005

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title = "Generalization behaviour of alkemic decision trees",

abstract = "This paper is concerned with generalization issues for a decision tree learner for structured data called ALKEMY. Motivated by error bounds established in statistical learning theory, we study the VC dimensions of some predicate classes defined on sets and multisets - two data-modelling constructs used intensively in the knowledge representation formalism of ALKEMY - and from that obtain insights into the (worst-case) generalization behaviour of the learner. The VC dimension results and the techniques used to derive them may be of wider independent interest.",

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AB - This paper is concerned with generalization issues for a decision tree learner for structured data called ALKEMY. Motivated by error bounds established in statistical learning theory, we study the VC dimensions of some predicate classes defined on sets and multisets - two data-modelling constructs used intensively in the knowledge representation formalism of ALKEMY - and from that obtain insights into the (worst-case) generalization behaviour of the learner. The VC dimension results and the techniques used to derive them may be of wider independent interest.

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AN - SCOPUS:26944449306

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 15th International Conference on Inductive Logic Programming, ILP 2005

Y2 - 10 August 2005 through 13 August 2005

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Generalization behaviour of alkemic decision trees

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