|
Abstract:
An adaptive on--line algorithm is proposed to estimate
hierarchical data structures for 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 on a metalearning
(learning of learning) idea to adapt to changing data
characteristics. Its efficiency is demonstrated by grouping
non--stationary artifical data and by hierarchical segmentation of
LANDSAT images.
|
