Semi-Supervised Learning of Mixture Models

Fabio Gagliardi Cozman; Ira Cohen; Marcelo Cesar Cirelo

Semi-Supervised Learning of Mixture Models

Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

168 Citas (SciVal)

Resumen

This paper analyzes the performance of semi-supervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this "degradation" phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled data. We discuss the impact of these theoretical results to practical situations.

Idioma original	Inglés estadounidense
Páginas	99-106
Número de páginas	8
Estado	Publicada - 1 dic. 2003
Publicado de forma externa	Sí
Evento	Proceedings, Twentieth International Conference on Machine Learning - Duración: 1 dic. 2003 → …

Conferencia

Conferencia	Proceedings, Twentieth International Conference on Machine Learning
Período	1/12/03 → …

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title = "Semi-Supervised Learning of Mixture Models",

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N2 - This paper analyzes the performance of semi-supervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this "degradation" phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled data. We discuss the impact of these theoretical results to practical situations.

AB - This paper analyzes the performance of semi-supervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations where additional labeled data would decrease classification error. We present a mathematical analysis of this "degradation" phenomenon and show that it is due to the fact that bias may be adversely affected by unlabeled data. We discuss the impact of these theoretical results to practical situations.

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T2 - Proceedings, Twentieth International Conference on Machine Learning

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Semi-Supervised Learning of Mixture Models

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