Automatically predicting age group and gender from face images acquired in unconstrained
conditions is an important and challenging task in many real-world applications …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

Class imbalance is a challenging problem in many classification tasks. It induces biased
classification results for minority classes that contain less training samples than others. Most …

For the purpose of enhancing the discriminability of convolutional neural networks (CNNs)
and facilitating the optimization, we investigate the activation function for a neural network …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

Fog computing is an encouraging technology in the coming generation to pipeline the
breach between cloud data centers and Internet of Things (IoT) devices. Fog computing is …

Multi-task learning (MTL) is a machine learning method to share knowledge for multiple
related machine learning tasks via learning those tasks jointly. It has been shown to be …

Recognizing prohibited items automatically is of great significance for intelligent X-ray
baggage security screening. Convolutional neural networks (CNNs), with the support of big …

Matrix factorization (MF) has been widely used to discover the low-rank structure and to
predict the missing entries of data matrix. In many real-world learning systems, the data …

Effective route planning is the key to improving transportation efficiency. By leveraging the in-
depth knowledge of road topology and traffic trends, experienced drivers (eg, taxi drivers) …