The performance of a new hybrid classifier based on boxes and nearest neighbors

Martin Anthony; Joel Ratsaby

The performance of a new hybrid classifier based on boxes and nearest neighbors

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as the 'sample width' of the classifier increases.

Original language	American English
State	Published - 1 Dec 2012
Externally published	Yes
Event	International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 - Fort Lauderdale, FL, United States Duration: 9 Jan 2012 → 11 Jan 2012

Conference

Conference	International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012
Country/Territory	United States
City	Fort Lauderdale, FL
Period	9/01/12 → 11/01/12

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Applied Mathematics

Cite this

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title = "The performance of a new hybrid classifier based on boxes and nearest neighbors",

abstract = "In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as the 'sample width' of the classifier increases.",

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language = "American English",

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AU - Ratsaby, Joel

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AB - In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as the 'sample width' of the classifier increases.

UR - http://www.scopus.com/inward/record.url?scp=84885770471&partnerID=8YFLogxK

M3 - Paper

T2 - International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012

Y2 - 9 January 2012 through 11 January 2012

ER -

The performance of a new hybrid classifier based on boxes and nearest neighbors

Abstract

Conference

All Science Journal Classification (ASJC) codes

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Cite this