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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 12, DECEMBER 2008 2053 Just-in-Time Adaptive Classifiers--Part II
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 12, DECEMBER 2008 2053
Just-in-Time Adaptive Classifiers--Part II:
Designing the Classifier
Cesare Alippi, Fellow, IEEE, and Manuel Roveri
Abstract--Aging effects, environmental changes, thermal drifts,
and soft and hard faults affect physical systems by changing their
nature and behavior over time. To cope with a process evolution
adaptive solutions must be envisaged to track its dynamics; in this
direction, adaptive classifiers are generally designed by assuming
the stationary hypothesis for the process generating the data with
very few results addressing nonstationary environments. This
paper proposes a methodology based on -nearest neighbor (NN)
classifiers for designing adaptive classification systems able to
react to changing conditions just-in-time (JIT), i.e., exactly when
it is needed. -NN classifiers have been selected for their compu-
tational-free training phase, the possibility to easily estimate the
model complexity and keep under control the computational
complexity of the classifier through suitable data reduction mecha-
nisms. A JIT classifier requires a temporal detection of a (possible)
process deviation (aspect tackled in a companion paper) followed

  

Source: Alippi, Cesare - Dipartimento di Elettronica e Informazione, Politecnico di Milano

 

Collections: Computer Technologies and Information Sciences