Kernel Dependency Estimation
Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Scholkopf, Vladimir N. Vapnik · 2002
We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using ker-nel functions, thus embedding the objects into vector spaces. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from par-tial images. 1