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Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
Predictive ApplicationPerformance Modeling in a Computational Grid Environment
, 1999
"... This paper describes and evaluates the application of three local learning algorithms  nearestneighbor, weightedaverage, and locallyweighted polynomial regression  for the prediction of runspecific resourceusage on the basis of runtime input parameters supplied to tools. A twolevel knowl ..."
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Cited by 81 (13 self)
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This paper describes and evaluates the application of three local learning algorithms  nearestneighbor, weightedaverage, and locallyweighted polynomial regression  for the prediction of runspecific resourceusage on the basis of runtime input parameters supplied to tools. A twolevel knowledge base allows the learning algorithms to track shortterm fluctuations in the performance of computing systems, and the use of instance editing techniques improves the scalability of the performancemodeling system. The learning algorithms assist PUNCH, a networkcomputing system at Purdue University, in emulating an ideal user in terms of its resource management and usage policies. 1. Introduction It is now recognized that the heterogeneous nature of the networkcomputing environment cannot be effectively exploited without some form of adaptive or demanddriven resource management (e.g., [10, 11, 12, 14, 18, 27]). A demanddriven resource management system can be characterized by its a...