119 citations · 294 across the 20 of their papers we have counts for
8 papers · 1 filter
Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks
Shiliang Sun, Rongqing Huang, Ya Gao
Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper does a lot of research on network-scale mode…
Kernel Regression with Sparse Metric Learning
Rongqing Huang, Shiliang Sun
Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the f…
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
Qingjiu Zhang, Shiliang Sun
Artificial neural network (ANN) is a very useful tool in solving learning problems. Boosting the performances of ANN can be mainly concluded from two aspects: optimizing the archit…
Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
Shiliang Sun, Changshui Zhang, Yi Zhang
A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial a…
Neural Network Multitask Learning for Traffic Flow Forecasting
Feng Jin, Shiliang Sun
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related…
A Survey on Multi-View Clustering
Guoqing Chao, Shiliang Sun, Jinbo Bi
With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data min…
Location Dependent Dirichlet Processes
Shiliang Sun, John Paisley, Qiuyang Liu
Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arisin…
Multi-view Regularized Gaussian Processes
Qiuyang Liu, Shiliang Sun
Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view l…