Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in th...
A variational level set method is developed for the supervised classification problem. Nonlinear classifier decision boundaries are obtained by minimizing an energy functional tha...
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
—Information about individuals on publicly available web sites stands as a valuable, yet unorganized, data source. Turning such an enormous data source into a “database” is h...