A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
Recent advances in statistical inference and machine learning close the divide between simulation and classical optimization, thereby enabling more rigorous and robust microarchit...
The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
Analyzing spend transactions is essential to organizations for understanding their global procurement. Central to this analysis is the automated classification of these transacti...
This paper proposes a mapping learning approach for caricature auto-generation. Simulating the artist’s creativity based on the object’s facial feature, our approach targets d...