In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Electroencephalographic (EEG) recordings can be of great help in decoding the open/close
hand's motion preparation. To this end, cortical EEG source signals in the motor cortex …

Introducing a handful of pairwise constraints into fuzzy clustering models to revise
memberships has been proven beneficial to boosting clustering performance. However …

MRI is a non-invasive imaging tool, accurate classification of brain tumours from MRI images
is a highly specialized area of a medical study. Classification of brain tumours is a method …

The graph convolutional networks (GCN) generalizes convolution neural networks into the
graph with an arbitrary topology structure. Since the geodesic function in the null space of …

Random feature is one successful technique to approximate traditional kernel functions, and
the random feature-based fuzzy clustering has been proved to be effective and efficient for …

Image clustering is a difficult task with important application value in computer vision. The
key to this task is the quality of images features. Most of current clustering methods …

A brain-computer interface (BCI) is a way for humans and machines to communicate directly.
It has been used in many successful ways over the last few decades. A wide variety of fields …

Feature extraction is a major challenge in operational reliability assessment, which requires
techniques and prior knowledge. Deep auto-encoder (DAE) is a popular deep learning …

Semantic segmentation and image parsing have rapidly become an eminent research area
in computer vision and machine learning domain. Many applications have required a robust …