Boosting with prior knowledge for call classification

Robert E. Schapire; Marie Rochery; Mazin Rahim; Narendra Gupta

doi:10.1109/TSA.2004.840937

Boosting with prior knowledge for call classification

Robert E. Schapire, Marie Rochery, Mazin Rahim, Narendra Gupta

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

The use of boosting for call classification in spoken language understanding is described in this paper. An extension to the AdaBoost algorithm is presented that permits the incorporation of prior knowledge of the application as a means of compensating for the large dependence on training data. We give a convergence result for the algorithm, and we describe experiments on four datasets showing that prior knowledge can substantially improve classification performance.

Original language	English (US)
Pages (from-to)	174-181
Number of pages	8
Journal	IEEE Transactions on Speech and Audio Processing
Volume	13
Issue number	2
DOIs	https://doi.org/10.1109/TSA.2004.840937
State	Published - Mar 2005
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Acoustics and Ultrasonics
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Keywords

Boosting
Call classification
Dialogue systems
Learning systems
Prior knowledge
Spoken language understanding

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10.1109/TSA.2004.840937

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abstract = "The use of boosting for call classification in spoken language understanding is described in this paper. An extension to the AdaBoost algorithm is presented that permits the incorporation of prior knowledge of the application as a means of compensating for the large dependence on training data. We give a convergence result for the algorithm, and we describe experiments on four datasets showing that prior knowledge can substantially improve classification performance.",

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AU - Rochery, Marie

AU - Rahim, Mazin

AU - Gupta, Narendra

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AB - The use of boosting for call classification in spoken language understanding is described in this paper. An extension to the AdaBoost algorithm is presented that permits the incorporation of prior knowledge of the application as a means of compensating for the large dependence on training data. We give a convergence result for the algorithm, and we describe experiments on four datasets showing that prior knowledge can substantially improve classification performance.

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KW - Dialogue systems

KW - Learning systems

KW - Prior knowledge

KW - Spoken language understanding

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Boosting with prior knowledge for call classification

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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