Most decision tree algorithms base their splitting decisions on a piecewise constant model. Often these splitting algorithms are extrapolated to trees with non-constant models at ...
David S. Vogel, Ognian Asparouhov, Tobias Scheffer
Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This pa...
VLIW processors are statically scheduled processors and their performance depends on the quality of the compiler’s scheduler. We propose a scheduling scheme where the applicatio...
Manvi Agarwal, S. K. Nandy, Jos T. J. van Eijndhov...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Abstract. Decision trees are considered as an efficient technique to express classification knowledge and to use it. However, their most standard algorithms do not deal with uncert...