Abstract
Learning strategies under covariate shift have recently been widely discussed. Under covariate shift, the density of learning inputs is different from that of test inputs. In such environments, learning machines need to employ special learning strategies to acquire a greater capability to generalize through learning.
However, incremental learning methods are also for learning in non-stationary learning environments, which would represent a kind of covariate-shift. However, the relation between covariate shift environments and incremental learning environments has not been adequately discussed.
This paper focuses on the covariate shift in incremental learning environments and our re-construction of a suitable incremental learning method.
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Yamauchi, K. (2009). Covariate Shift and Incremental Learning. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_140
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DOI: https://doi.org/10.1007/978-3-642-02490-0_140
Publisher Name: Springer, Berlin, Heidelberg
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