We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
In this paper we propose a surface reconstruction method for highly noisy and non-uniform data based on minimal surface model and tensor voting method. To deal with ill-posedness, ...
DanFeng Lu, HongKai Zhao, Ming Jiang 0001, ShuLin ...
Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, ...
In this paper, we proposed a new surface-based fractal information dimension (FID) method to quantify the cortical complexity. Unlike the traditional box-counting method to measure...
Yuanchao Zhang, Jiefeng Jiang, Lei Lin, Feng Shi, ...
Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as w...
S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang...