Commit 5607c15f authored by ehebrard's avatar ehebrard
Browse files

abstract

parent 54b4477b
......@@ -78,8 +78,8 @@
\begin{abstract}
In this paper we introduce a {simple} algorithm to learn optimal decision trees of bounded depth. This algorithm, \budalg, is as memory and time efficient as heuristics, and yet more efficient than most exact methods on most data sets.
Its worst case time complexity is the same as state-of-the-art dynamic programming methods however its anytime behavior is vastly superior.
Experiments show that whereas existing exact methods hardly scale to deep trees, \budalg\ learns, without significant computational overhead, trees comparable to standard heuristics, and can significantly improve their accuracy when given more computation time.
Its worst case time complexity is the same as state-of-the-art dynamic programming methods. However, its anytime behavior is vastly superior.
Experiments show that whereas existing exact methods hardly scale to deep trees, our algorithm learns trees comparable to standard heuristics without significant computational overhead, and can significantly improve their accuracy when given more computation time.
% State-of-the-art exact methods often have poor anytime behavior, and hardly scale to deep trees.
% Experiments show that they are typically orders of magnitude slower than the proposed algorithm to compute optimally accurate classifiers of a given depth.
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment