Incremental Learning Based on Growing Gaussian Mixture Models

A. Bouchachia, C. Vanaret
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引用次数: 31

Abstract

Incremental learning aims at equipping data-driven systems with self-monitoring and self-adaptation mechanisms to accommodate new data in an online setting. The resulting model underlying the system can be adjusted whenever data become available. The present paper proposes a new incremental learning algorithm, called 2G2M, to learn Growing Gaussian Mixture Models. The algorithm is furnished with abilities (1) to accommodate data online, (2) to maintain low complexity of the model, and (3) to reconcile labeled and unlabeled data. To discuss the efficiency of the proposed incremental learning algorithm, an empirical evaluation is provided.
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基于增长高斯混合模型的增量学习
增量学习旨在为数据驱动的系统配备自我监测和自适应机制,以适应在线环境中的新数据。当数据可用时,可以调整系统底层的生成模型。本文提出了一种新的增量学习算法,称为2G2M,用于学习高斯混合增长模型。该算法具有以下能力:(1)适应在线数据;(2)保持模型的低复杂度;(3)调和标记和未标记数据。为了讨论所提出的增量学习算法的效率,提供了一个经验评估。
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