Adapting to Drift in Continuous Domains
نویسندگان
چکیده
The paper presents the system FRANN, which exploits the idea of radial-basis functions for the needs of learning in numeric domains under concept drift. The classiication accuracy of the program compares favourably to that of older algorithms that are based on symbol manipulation. The system tolerates noise and is able to learn symbolic, numeric , and mixed concepts with nonlinear boundaries in environments with abrupt as well as gradual concept drift.
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