Semi-supervised learning with a growing self-organizing map (GSOM) is commonly used to cope with the machine

learning problems. The performance of semi-supervised GSOM is associated with the structure of clustering ...

. A weaker, yet still very useful option is providing constraints on the unlabeled training samples, which is the focus of the Constrained Semi-Supervised (CSS) clustering. On the other hand, online learning has gained considerable amount of interests...

In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian ...

of these feature maps leads to mutually linearly independent dimensions of feature space, hence, reduce the redundancy in this space. These feature maps can be applied to single-pass online learning methods with $l_2$- and $l_0$-norm regularization to reduce...

Semi-Supervised Growing Self Organizing Map (SSGSOM) is one of the best methods for online classification with partial labeled data. Many parameters can affect the performance of this method. The structure of GSOM network, ...

-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of Auto...

high-dimensional damage-sensitive features under environmental and operational variability. The key
novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms
for extracting...