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首页> 外文期刊>IEEE Transactions on Neural Networks >Incremental Learning of Chunk Data for Online Pattern Classification Systems
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Incremental Learning of Chunk Data for Online Pattern Classification Systems

机译：在线模式分类系统的块数据增量学习

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摘要

This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where training samples are presented only once. For this purpose, we have extended incremental principal component analysis (IPCA) and some classifier models were effectively combined with it. However, there was a drawback in this approach that training samples must be learned one by one due to the limitation of IPCA. To overcome this problem, we propose another extension of IPCA called chunk IPCA in which a chunk of training samples is processed at a time. In the experiments, we evaluate the classification performance for several large-scale data sets to discuss the scalability of chunk IPCA under one-pass incremental learning environments. The experimental results suggest that chunk IPCA can reduce the training time effectively as compared with IPCA unless the number of input attributes is too large. We study the influence of the size of initial training data and the size of given chunk data on classification accuracy and learning time. We also show that chunk IPCA can obtain major eigenvectors with fairly good approximation.
机译：本文提出了一种模式分类系统，其中特征提取和分类器学习不仅可以在线进行，而且可以一次通过，训练样本仅一次出现。为此，我们扩展了增量主成分分析（IPCA），并将一些分类器模型与之有效结合。但是，由于IPCA的局限性，该方法存在一个缺点，即必须逐个学习训练样本。为了克服这个问题，我们提出了IPCA的另一扩展，称为块IPCA，其中一次处理了一块训练样本。在实验中，我们评估了几个大型数据集的分类性能，以讨论一遍式增量学习环境下块IPCA的可伸缩性。实验结果表明，与IPCA相比，大块IPCA可以有效地减少训练时间，除​​非输入属性的数量太大。我们研究了初始训练数据的大小和给定块数据的大小对分类准确性和学习时间的影响。我们还表明，大块IPCA可以获得具有相当好的近似值的主要特征向量。

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