Adaptive learning algorithm for pattern classification

机译：模式分类的自适应学习算法

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摘要

In this paper, a pattern classification task was regarded as a sample selection problem where a sparse subset of sample from the labeled training set was chosen. We proposed an adaptive learning algorithm utilizing the least square function to address this problem. Using these selected samples, which we call informative vectors, a classifier capable of recognizing the test samples was established. This novel algorithm is a combination of searching strategies that, not only based on forward searching steps, but adaptively takes backward steps to correct the errors introduced by earlier forward steps. We experimentally demonstrated on face image and text dataset that classifier using such informative vectors outperformed other methods.

机译：在本文中，将模式分类任务视为示例选择问题，其中选择了来自标记训练集的样本稀疏子集。我们提出了一种利用最小二乘函数来解决这个问题的自适应学习算法。使用这些所选样本，我们呼叫信息载体，建立了能够识别测试样本的分类器。这种新颖算法是搜索策略的组合，不仅基于前向搜索步骤，而且自适应地返回步骤以纠正早期前进步骤引入的错误。我们在实验上展示了使用此类信息等待的分类器的面部图像和文本数据集表现出其他方法。

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《IEEE International Conference on Information and Automation》|2013年||共3页
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作者
Maohu Zhu; Nanfeng Jie; Tianzi Jiang;
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正文语种
中图分类自动化技术及设备;
关键词
face recognition; informative vector; pattern classification; sample selection; sparse representation; text categorization;

机译：面部识别;信息矢量;模式分类;样本选择;稀疏表示;文本分类;

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Adaptive learning algorithm for pattern classification

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