Linear and kernel discriminant analyses are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analyses have been proposed to ove...
With the advance of hardware and communication technologies, stream time series is gaining ever-increasing attention due to its importance in many applications such as financial da...
The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much t...
Cosimo Palmisano, Alexander Tuzhilin, Michele Gorg...
For many practical learning scenarios, the integrated use of more than one learning tool is educationally beneficial. In these cases, interoperability between learning tools--getti...
Andreas Harrer, Niels Pinkwart, Bruce M. McLaren, ...
Abstract--We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Rie...