A resource-allocating network for function interpolation
Neural Computation
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
Constructive Incremental Learning from Only Local Information
Neural Computation
Incremental Leaning and Model Selection for Radial Basis Function Network through Sleep
IEICE - Transactions on Information and Systems
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental learning methods with retrieving of interfered patterns
IEEE Transactions on Neural Networks
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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.