&lt;title&gt;Software tool for developing algorithms for surface inspection systems&lt;/title&gt;

Harkonen, Ari K.; Mitikka, Risto; Moring, Ilkka

doi:10.1117/12.194352

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

1998

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Decision tree learning is a widely used method for pattem recognition and image interpretation [10] [11] [13]. Properly addressing the discretization process of continuous-valued features is an important problem during decision tree learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-interval discretization methods for decision tree learning

Perner

Trautzsch

1998

Advances in Pattern Recognition

View full text Add to dashboard Cite

Abstract. Properly addressing the discretization process of continuos valued features is an important problem dtu~g decision tree learning. This paper describes four multi-interval discretization methods for induction of decision trees used in dynamic fashion. We compare two known discretization methods to two new methods proposed in this paper based on a histogram based method and a neural net based method (LVQ). We compare them according to accuracy of the resulting decision tree and to compactness of the tree. For our comparison we used three data bases, IRIS domain, satellite domain and OHS domain (ovariel hyper stimulation).

show abstract

Section: Introductionmentioning

confidence: 99%