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MCS
2007
Springer
14 years 1 months ago
An Ensemble Approach for Incremental Learning in Nonstationary Environments
Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presente...
Michael Muhlbaier, Robi Polikar
AAAI
2008
13 years 9 months ago
Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders
Usually a voting rule requires agents to give their preferences as linear orders. However, in some cases it is impractical for an agent to give a linear order over all the alterna...
Lirong Xia, Vincent Conitzer
IEAAIE
2010
Springer
13 years 5 months ago
Learning User Preferences to Maximise Occupant Comfort in Office Buildings
It is desirable to ensure that the thermal comfort conditions in offices are in line with the preferences of occupants. Controlling their offices correctly therefore requires the c...
Anika Schumann, Nic Wilson, Mateo Burillo
KDD
2003
ACM
129views Data Mining» more  KDD 2003»
14 years 8 months ago
Empirical comparisons of various voting methods in bagging
Finding effective methods for developing an ensemble of models has been an active research area of large-scale data mining in recent years. Models learned from data are often subj...
Kelvin T. Leung, Douglas Stott Parker Jr.
JMLR
2010
186views more  JMLR 2010»
13 years 2 months ago
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...
Philippos Mordohai, Gérard G. Medioni