Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
A multiresolution data decomposition offers a fundamental framework supporting compression, progressive transmission, and level-of-detail (LOD) control for large two or three dime...
Wenli Cai, Georgios Sakas, Roberto Grosso, Thomas ...
A novel machine language genetic programming system that uses one-dimensional core memories is proposed and simulated. The core is compared to a biochemical reaction space, and in ...
Hierarchical clustering is used widely to organize data and search for patterns. Previous algorithms assume that the body of data being clustered is fixed while the algorithm runs...
H. Van Dyke Parunak, Richard Rohwer, Theodore C. B...
Enhancing volume visualization with additional cues from our sense of touch has shown the potential to increase both speed and accuracy in the data exploration. Research in the ar...