Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis
Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis
复制标题
用于分层数据分析的决策树无监督在线学习
DOI：
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发表时间：
1997
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影响因子：
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通讯作者：
J. Buhmann
中科院分区：
文献类型：
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作者：
Marcus Held;J. Buhmann
针对非平稳数据源，提出了一种自适应在线估计分层数据结构的算法。该方法基于最小交叉熵原理导出数据聚类的决策树，并采用元学习思想（学习学习）来适应数据特征的变化。通过对非平稳人工数据进行分组和对LANDSAT图像进行分层分割，验证了该方法的有效性。
An adaptive on-line algorithm is proposed to estimate hierarchical data structures for non-stationary data sources. The approach is based on the principle of minimum cross entropy to derive a decision tree for data clustering and it employs a metalearning idea (learning to learn) to adapt to changes in data characteristics. Its efficiency is demonstrated by grouping non-stationary artifical data and by hierarchical segmentation of LANDSAT images.