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KDD
2002
ACM
171views Data Mining» more  KDD 2002»
14 years 5 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
ICSNC
2007
IEEE
13 years 11 months ago
Movement Prediction Using Bayesian Learning for Neural Networks
Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path pr...
Sherif Akoush, Ahmed Sameh
IROS
2008
IEEE
211views Robotics» more  IROS 2008»
13 years 11 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
KDD
2002
ACM
136views Data Mining» more  KDD 2002»
14 years 5 months ago
Relational Markov models and their application to adaptive web navigation
Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain ...
Corin R. Anderson, Pedro Domingos, Daniel S. Weld
ICDM
2010
IEEE
267views Data Mining» more  ICDM 2010»
13 years 3 months ago
Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model
Web-page recommendation is to predict the next request of pages that Web users are potentially interested in when surfing the Web. This technique can guide Web users to find more u...
Qingyan Yang, Ju Fan, Jianyong Wang, Lizhu Zhou