This paper is about the use of metric data structures in high-dimensionalor non-Euclidean space to permit cached sufficientstatisticsaccelerationsof learning algorithms. It has re...
We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect bia...
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
A very important class of queries in GIS applications is the class of K-Nearest Neighbor queries. Most of the current studies on the K-Nearest Neighbor queries utilize spatial ind...
Cyrus Shahabi, Mohammad R. Kolahdouzan, Mehdi Shar...