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COLT
2003
Springer
14 years 3 months ago
Learning Random Log-Depth Decision Trees under the Uniform Distribution
We consider three natural models of random logarithmic depth decision trees over Boolean variables. We give an efficient algorithm that for each of these models learns all but an ...
Jeffrey C. Jackson, Rocco A. Servedio
ALT
2004
Springer
14 years 6 months ago
Learning r-of-k Functions by Boosting
We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answer “+1” if at least r o...
Kohei Hatano, Osamu Watanabe
AIA
2006
13 years 11 months ago
Speeding Up Model-based Diagnosis by a Heuristic Approach to Solving SAT
Model-based diagnosis of technical systems requires both a simulation machinery and a logic calculus. The former is responsible for the system's behavior analysis, the latter...
Benno Stein, Oliver Niggemann, Theodor Lettmann
NIPS
2003
13 years 11 months ago
Learning the k in k-means
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an impr...
Greg Hamerly, Charles Elkan
ICONIP
1998
13 years 11 months ago
Inducing Relational Concepts with Neural Networks via the LINUS System
This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken...
Rodrigo Basilio, Gerson Zaverucha, Artur S. d'Avil...