@incollection {boracchi2010ICANN,
   author = {Alippi, Cesare and Boracchi, Giacomo and Roveri, Manuel},
   affiliation = {Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy},
   title = {Adaptive Classifiers with ICI-Based Adaptive Knowledge Base Management},
   booktitle = {Artificial Neural Networks – ICANN 2010},
   series = {Lecture Notes in Computer Science},
   editor = {Diamantaras, Konstantinos and Duch, Wlodek and Iliadis, Lazaros},
   publisher = {Springer Berlin / Heidelberg},
   isbn = {},
   pages = {458-467},
   volume = {6353},
   url = {http://dx.doi.org/10.1007/978-3-642-15822-3_56},
   note = {10.1007/978-3-642-15822-3_56},
   abstract = {Classification systems meant to operate in non-stationary environments are requested to adapt when the process generating the observed data changes. A particularly effective form of adaptation in the abrupt perturbation case suggests to release the obsolete knowledge base of the classifier (or training set), and consider novel samples to configure the new classification model. In this direction, we propose an adaptive classifier based on a change detection test used both for detecting changes in the process and identifying the new training set (and, then, the new classifier). A key point of the proposed solution is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process generating the data evolves through a sequence of abrupt changes.},
   year = {2010}
}