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ML
2006
ACM
131views Machine Learning» more  ML 2006»
15 years 3 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
144
Voted
ICALT
2009
IEEE
15 years 1 months ago
Eye-Tracking Users' Behavior in Relation to Cognitive Style within an E-learning Environment
Eye-tracking measurements may be used as a method of identifying users' actual behavior in a hypermedia setting. In this research, an eye-tracking experiment was conducted in...
Nikos Tsianos, Panagiotis Germanakos, Zacharias Le...
157
Voted
WWW
2011
ACM
14 years 10 months ago
Parallel boosted regression trees for web search ranking
Gradient Boosted Regression Trees (GBRT) are the current state-of-the-art learning paradigm for machine learned websearch ranking — a domain notorious for very large data sets. ...
Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal...
115
Voted
COLT
2007
Springer
15 years 9 months ago
Online Learning with Prior Knowledge
The standard so-called experts algorithms are methods for utilizing a given set of “experts” to make good choices in a sequential decision-making problem. In the standard setti...
Elad Hazan, Nimrod Megiddo
COLT
1991
Springer
15 years 7 months ago
Learning Probabilistic Read-Once Formulas on Product Distributions
Abstract. This paper presents a polynomial-time algorithm for inferring a probabilistic generalization of the class of read-once Boolean formulas over the usual basis {AND,OR,NOT}....
Robert E. Schapire