Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing ...
Shin et al [19] and McKay et al [15] previously applied tree compression and semantics-based simplification to study the distribution of building blocks in evolving Genetic Progr...
Robert I. McKay, Xuan Hoai Nguyen, James R. Cheney...
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train diffe...
The best recent supervised sequence learning methods use gradient descent to train networks of miniature nets called memory cells. The most popular cell structure seems somewhat ar...