Decision trees that are limited to testing a single variable at a node are potentially much larger than trees that allow testing multiple variables at a node. This limitation redu...
: Whiledecision tree compilationis a promisingway tocarry out guard tests e ciently, the methods given in the literature do not take into account either the execution characteristi...
: The problem of transforming the knowledge bases of performance systems using induced rules or decision trees into comprehensible knowledgestructures is addressed. A knowledgestru...
Abstract. We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each o...
Abstract: An emerging practice in e-commerce systems is to conduct interviews with buyers in order to identify their needs. The goal of such an interview is to determine sets of pr...
The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the “large” Fourier coefficients of a Boolean function. It is the main tool for learning decision trees ...
The primary objective of disparities research is to model the differences across multiple groups and identify the groups that behave significantly different from each other. Indep...
Indranil Palit, Chandan K. Reddy, Kendra L. Schwar...
We describe an efficient implementation (MRDTL-2) of the Multi-relational decision tree learning (MRDTL) algorithm [23] which in turn was based on a proposal by Knobbe et al. [19] ...
Web search engines work well for finding crawlable pages, but not for finding datasets hidden behind Web search forms. We describe a novel technique for detecting search forms, ...
This paper describes a novel approach using Hidden Markov Models (HMM) to detect complex Internet attacks. These attacks consist of several steps that may occur over an extended pe...
Dirk Ourston, Sara Matzner, William Stump, Bryan H...