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123
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.
A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk
, 1997
"... This article presents a technique for nonparametrically estimating continuoustime di#usion processes which are observed at discrete intervals. We illustrate the methodology by using daily three and six month Treasury Bill data, from January 1965 to July 1995, to estimate the drift and di#usion of t ..."
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Cited by 207 (5 self)
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This article presents a technique for nonparametrically estimating continuoustime di#usion processes which are observed at discrete intervals. We illustrate the methodology by using daily three and six month Treasury Bill data, from January 1965 to July 1995, to estimate the drift and di#usion of the short rate, and the market price of interest rate risk. While the estimated di#usion is similar to that estimated by Chan, Karolyi, Longsta# and Sanders (1992), there is evidence of substantial nonlinearity in the drift. This is close to zero for low and medium interest rates, but mean reversion increases sharply at higher interest rates.
Statistical prediction of task execution times through analytic benchmarking for Computer Engineering and Intelligent Systems www.iiste.org
 ISSN 22221719 (Paper) ISSN 22222863 (Online) Vol 3, No.4
, 1999
"... In this paper, a method for estimating task execution times is presented, in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution ..."
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Cited by 56 (2 self)
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In this paper, a method for estimating task execution times is presented, in order to facilitate dynamic scheduling in a heterogeneous metacomputing environment. Execution time is treated as a random variable and is statistically estimated from past observations. This method predicts the execution time as a function of several parameters of the input data, and does not require any direct information about the algorithms used by the tasks or the architecture of the machines. Techniques based upon the concept of analytic benchmarking/code profiling [7] are used to accurately determine the performance differences between machines, allowing observations to be shared between machines. Experimental results using real data are presented.
A New Class of Nonstationary Spatial Models
"... Spatial processes are an important modeling tool for many problems of environmental monitoring. Classical geostatistics is based on processes which are stationary and isotropic, but it is widely recognized that real environmental processes are rarely stationary and isotropic. In this paper, a new cl ..."
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Cited by 34 (3 self)
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Spatial processes are an important modeling tool for many problems of environmental monitoring. Classical geostatistics is based on processes which are stationary and isotropic, but it is widely recognized that real environmental processes are rarely stationary and isotropic. In this paper, a new class of nonstationary processes is proposed, based on a convolution of local stationary processes. This model has the advantage that the model is simultaneously defined everywhere, unlike \moving window" approaches, but it retains the attractive property that locally in small regions, it behaves like a stationary spatial processes. We discuss model fitting through exact and approximate likelihood maximization, and propose a hierarchical Bayes approach to allow predictive inference when the parameters of the model are unknown. Applications include obtaining the total loading of sulfur dioxide concentrations over different geopolitical boundaries.
Optimal rates of convergence to Bayes risk in nonparametric discrimination
 Ph.D. Dissertation, UCLA
, 1982
"... Consider the multiclassification (discrimination) problem with known prior probabilities and a multidimensional vector of observations. Assume the underlying densities corresponding to the various classes are unknown but a training sample of size N is available from each class. Rates of convergence ..."
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Cited by 25 (1 self)
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Consider the multiclassification (discrimination) problem with known prior probabilities and a multidimensional vector of observations. Assume the underlying densities corresponding to the various classes are unknown but a training sample of size N is available from each class. Rates of convergence to Bayes Risk are investigated under smoothness conditions on the underlying densities of the type often seen in nonparametric density estimation. These rates can be drastically affected by a small change in the prior probabilities, so the error criterion used here is Bayes Risk • ~ averaged (uniformly) over all prior probabilities. Then it is shown that a certain rate, N r, is optimal in the sense that no rule can do better (uniformly over the class of smooth densities) and a rule is exhibited which does that well. The optimal value of r depends on the smoothness and the dimensionality of the observations in the same way as for nonparametric density estimation with integrated square error loss. 1• I NTRODUCTI ON The classification or discrimination problem arises whenever one wants to assign an object to one of a finite number of classes based on a vector of d measurements. More precisely, let f
Recursive Lazy Learning for Modeling and Control
 IN: MACHINE LEARNING: ECML98 (10TH EUROPEAN CONFERENCE ON MACHINE LEARNING
, 1998
"... This paper presents a local method for modeling and control of nonlinear dynamical systems, when only a limited amount of inputoutput data is available. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear o ..."
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Cited by 21 (2 self)
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This paper presents a local method for modeling and control of nonlinear dynamical systems, when only a limited amount of inputoutput data is available. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory. The local modeling procedure uses a querybased approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non linear systems in far from equilibrium configurations. Simulation results of the identification of a nonlinear benchmark model and of the control of a complex nonlinear system (the bioreactor) are presented.
The local paradigm for modeling and control: from neurofuzzy to lazy learning. Fuzzy Sets and Systems
, 2001
"... to lazy learning ..."
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Databaseguided simultaneous multislice 3D segmentation for volumetric data
 In ECCV
, 2006
"... Abstract. Automatic delineation of anatomical structures in 3D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. This makes it increasingly difficult to encode prior knowledge about the object segmentation in ..."
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Cited by 18 (8 self)
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Abstract. Automatic delineation of anatomical structures in 3D volumetric data is a challenging task due to the complexity of the object appearance as well as the quantity of information to be processed. This makes it increasingly difficult to encode prior knowledge about the object segmentation in a traditional formulation as a perceptual grouping task. We introduce a fast shape segmentation method for 3D volumetric data by extending the 2D databaseguided segmentation paradigm which directly exploits expert annotations of the interest object in large medical databases. Rather than dealing with 3D data directly, we take advantage of the observation that the information about position and appearance of a 3D shape can be characterized by a set of 2D slices. Cutting these multiple slices simultaneously from the 3D shape allows us to represent and process 3D data as efficiently as 2D images while keeping most of the information about the 3D shape. To cut slices consistently for all shapes, an iterative 3D nonrigid shape alignment method is also proposed for building local coordinates for each shape. Features from all the slices are jointly used to learn to discriminate between the object appearance and background and to learn the association between appearance and shape. The resulting procedure is able to perform shape segmentation in only a few seconds. Extensive experiments on cardiac ultrasound images demonstrate the algorithm’s accuracy and robustness in the presence of large amounts of noise. 1