Weishan Dong

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Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs’ performance in the context of global optimization. So far, the studies or applications of multivariate probabilistic model based continuous EDAs are still restricted to rather low dimensional problems (smaller than 100D). Traditional EDAs have(More)
Multivariate Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for Multivariate Gaussian based EDAs (MGEDAs), named Eigen Decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen(More)
Since the estimation of distribution algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian models as an example, EDAs based on single Gaussian distribution have good performance on solving simple unimodal functions and multimodal functions whose landscape has an obvious trend(More)
This paper proposes a novel gender recognition method based on the head-shoulder part of human body. The head-shoulder area contains much information that could be cues to infer the gender of a person, such as hair-style, face, neckline style and so on. A rich high-dimensional feature descriptor is designed to extract gradient, texture and orientation(More)
Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs). In this paper, we analyze continuous EDAs and show that they don’t always work because of computation error: covariance matrix of Gaussian model can be ill-posed and Gaussian based EDAs using full covariance matrix will fail under specific conditions. It is a(More)
We address the problem of model based recognition. Our aim is to localize and recognize road vehicles from monocular images in calibrated scenes. A deformable 3D geometric vehicle model with 12 parameters is set up as prior information and Bayesian Classification Error is adopted for evaluation of fitness between the model and images. Using a novel(More)
Detecting significant overdensity or underdensity clusters in spatio-temporal data is critical for many real-world applications. Most existing approaches are designed to deal with regularly shaped clusters such as circular, elliptic and rectangular ones, but cannot work well on irregularly shaped clusters. In this paper, we propose GridScan, a grid-based(More)
Multi-task learning is a paradigm, where multiple tasks are jointly learnt. Previous multi-task learning models usually treat all tasks and instances per task equally during learning. Inspired by the fact that humans often learn from easy concepts to hard ones in the cognitive process, in this paper, we propose a novel multi-task learning framework that(More)
Water pipe failures can not only have a great impact on people’s daily life but also cause significant waste of water which is an essential and precious resource to human beings. As a result, preventative maintenance for water pipes, particularly in urbanscale networks, is of great importance for a sustainable society. To achieve effective replacement and(More)
In this paper, we propose a novel feature selection-based method for facial age estimation. The face aging is a typical temporal process, and facial images should have certain ordinal patterns in the aging feature space. From the geometrical perspective, a facial image can be usually seen as sampled from a low-dimensional manifold embedded in the original(More)