With Internet of Things (IoT) being prevalently adopted in recent years, traditional machine
learning and data mining methods can hardly be competent to deal with the complex big …

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need
for machine learning techniques which are well-suited for these different datasets. One …

We generalize the traditional supervised self-organizing map to supervised kernel self-
organizing map by incorporating the kernel function to further improve its capability of …

Self-Organizing Map (SOM) is one of the neural networks employed, particularly in data
mining and related fields. Like Deep Learning, SOM requires iterative calculations of a vast …

Self-organizing map (SOM) is an unsupervised artificial neural network which is used for
data visualization and dimensionality reduction purposes. Multiple self-organizing maps …

Plenty of data are generated continuously due to the progress in the field of network
technology. Additionally, some data contain substantial noise, while other data vary their …

Kernel function implicitly maps data from its original space to a higher dimensional feature
space. Kernel based machine learning algorithms are typically applied to data that is not …

Abstract Self-Organizing Maps (SOM) are unsupervised learning neural networks. They
provide a mapping from high-dimensional data onto a lower dimensional output map, while …

In this paper, we first design a more generalized network model, Improved circular back
propagation (CBP), based on the same structure as CBP proposed by Ridella et al. The …

Kernel methods are popular due to their solid and well-understood theoretical foundation.
However, kernel-based learning methods generally have considerable memory and …