TY - JOUR

T1 - Large width nearest prototype classification on general distance spaces

AU - Anthony, Martin

AU - Ratsaby, Joel

N1 - Publisher Copyright:
© 2018 Elsevier B.V.

PY - 2018/8/22

Y1 - 2018/8/22

N2 - In this paper we consider the problem of learning nearest-prototype classifiers in any finite distance space; that is, in any finite set equipped with a distance function. An important advantage of a distance space over a metric space is that the triangle inequality need not be satisfied, which makes our results potentially very useful in practice. We consider a family of binary classifiers for learning nearest-prototype classification on distance spaces, building on the concept of large-width learning which we introduced and studied in earlier works. Nearest-prototype is a more general version of the ubiquitous nearest-neighbor classifier: a prototype may or may not be a sample point. One advantage in the approach taken in this paper is that the error bounds depend on a ‘width’ parameter, which can be sample-dependent and thereby yield a tighter bound.

AB - In this paper we consider the problem of learning nearest-prototype classifiers in any finite distance space; that is, in any finite set equipped with a distance function. An important advantage of a distance space over a metric space is that the triangle inequality need not be satisfied, which makes our results potentially very useful in practice. We consider a family of binary classifiers for learning nearest-prototype classification on distance spaces, building on the concept of large-width learning which we introduced and studied in earlier works. Nearest-prototype is a more general version of the ubiquitous nearest-neighbor classifier: a prototype may or may not be a sample point. One advantage in the approach taken in this paper is that the error bounds depend on a ‘width’ parameter, which can be sample-dependent and thereby yield a tighter bound.

KW - Distance space

KW - LVQ

KW - Large margin learning

KW - Metric space

KW - Nearest-neighbor classification

UR - http://www.scopus.com/inward/record.url?scp=85046809141&partnerID=8YFLogxK

U2 - 10.1016/j.tcs.2018.04.045

DO - 10.1016/j.tcs.2018.04.045

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AN - SCOPUS:85046809141

SN - 0304-3975

VL - 738

SP - 65

EP - 79

JO - Theoretical Computer Science

JF - Theoretical Computer Science

ER -