The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast ...
This paper characterizes the polynomial time learnability of TPk, the class of collections of at most k rst-order terms. A collection in TPk denes the union of the languages den...
In regular inference, the problem is to infer a regular language, typically represented by a deterministic finite automaton (DFA) from answers to a finite set of membership querie...
We give a new model of learning motivated by smoothed analysis (Spielman and Teng, 2001). In this model, we analyze two new algorithms, for PAC-learning DNFs and agnostically learn...
Adam Tauman Kalai, Alex Samorodnitsky, Shang-Hua T...