TY - GEN

T1 - Classification in non-metric spaces

AU - Weinshall, Daphna

AU - Jacobs, David W.

AU - Gdalyahu, Yoram

PY - 1999

Y1 - 1999

N2 - A key question in vision is how to represent our knowledge of previously encountered objects to classify new ones. The answer depends on how we determine the similarity of two objects. Similarity tells us how relevant each previously seen object is in determining the category to which a new object belongs. Here a dichotomy emerges. Complex notions of similarity appear necessary for cognitive models and applications, while simple notions of similarity form a tractable basis for current computational approaches to classification. We explore the nature of this dichotomy and why it calls for new approaches to well-studied problems in learning. We begin this process by demonstrating new computational methods for supervised learning that can handle complex notions of similarity. (1) We discuss how to implement parametric methods that represent a class by its mean when using non-metric similarity functions; and (2) We review non-parametric methods that we have developed using nearest neighbor classification in non-metric spaces. Point (2), and some of the background of our work have been described in more detail in [8].

AB - A key question in vision is how to represent our knowledge of previously encountered objects to classify new ones. The answer depends on how we determine the similarity of two objects. Similarity tells us how relevant each previously seen object is in determining the category to which a new object belongs. Here a dichotomy emerges. Complex notions of similarity appear necessary for cognitive models and applications, while simple notions of similarity form a tractable basis for current computational approaches to classification. We explore the nature of this dichotomy and why it calls for new approaches to well-studied problems in learning. We begin this process by demonstrating new computational methods for supervised learning that can handle complex notions of similarity. (1) We discuss how to implement parametric methods that represent a class by its mean when using non-metric similarity functions; and (2) We review non-parametric methods that we have developed using nearest neighbor classification in non-metric spaces. Point (2), and some of the background of our work have been described in more detail in [8].

UR - http://www.scopus.com/inward/record.url?scp=34547991854&partnerID=8YFLogxK

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AN - SCOPUS:34547991854

SN - 0262112450

SN - 9780262112451

T3 - Advances in Neural Information Processing Systems

SP - 838

EP - 844

BT - Advances in Neural Information Processing Systems 11 - Proceedings of the 1998 Conference, NIPS 1998

PB - Neural information processing systems foundation

T2 - 12th Annual Conference on Neural Information Processing Systems, NIPS 1998

Y2 - 30 November 1998 through 5 December 1998

ER -