We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
Abstract: Kernel classifiers based on Support Vector Machines (SVM) have achieved state-ofthe-art results in several visual classification tasks, however, recent publications and d...
Guo ShengYang, Min Tan, Si-Yao Fu, Zeng-Guang Hou,...
Modern geographic databases can contain a large volume of data that need to be distributed to subscribed customers. The data can be modeled as a cube, where typical dimensions inc...
We describe and analyze a simple and effective iterative algorithm for solving the optimization problem cast by Support Vector Machines (SVM). Our method alternates between stocha...
We present a polynomial time randomized algorithm for global value numbering. Our algorithm is complete when conditionals are treated as non-deterministic and all operators are tr...