Artificial Intelligence White Papers
Tracking Concept Drifting With an Online-Optimized Incremental Learning Framework
Overview Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series such as video streams over a relatively long period of time. An Online-Optimized Incremental Learning framework is proposed as an example learning system for tracking the drifting concepts. Furthermore, a set of measures are defined to track the process of concept drifting in the learning system. These tracking measures are also applied to determine the corresponding parameters used for model updating in order to obtain the optimal up-to-date classifiers.
| Publisher | Association for Computing Machinery | File Format | |
|---|---|---|---|
| Date Published | November 2005 | ||
| Format | White Papers | ||
| Topics | |||
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