Results 1  10
of
442
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 ..."
Abstract

Cited by 448 (52 self)
 Add to MetaCart
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.
Does code decay? Assessing the evidence from change management data
 In IEEE Transactions on Software Engineering
, 2001
"... AbstractÐA central feature of the evolution of large software systems is that changeÐwhich is necessary to add new functionality, accommodate new hardware, and repair faultsÐbecomes increasingly difficult over time. In this paper, we approach this phenomenon, which we term code decay, scientifically ..."
Abstract

Cited by 145 (12 self)
 Add to MetaCart
AbstractÐA central feature of the evolution of large software systems is that changeÐwhich is necessary to add new functionality, accommodate new hardware, and repair faultsÐbecomes increasingly difficult over time. In this paper, we approach this phenomenon, which we term code decay, scientifically and statistically. We define code decay and propose a number of measurements (code decay indices) on software and on the organizations that produce it, that serve as symptoms, risk factors, and predictors of decay. Using an unusually rich data set (the fifteenplus year change history of the millions of lines of software for a telephone switching system), we find mixed, but on the whole persuasive, statistical evidence of code decay, which is corroborated by developers of the code. Suggestive indications that perfective maintenance can retard code decay are also discussed. Index TermsÐSoftware maintenance, metrics, statistical analysis, fault potential, span of changes, effort modeling. æ 1
Polynomial Splines and Their Tensor Products in Extended Linear Modeling
 Ann. Statist
, 1997
"... ANOVA type models are considered for a regression function or for the logarithm of a probability function, conditional probability function, density function, conditional density function, hazard function, conditional hazard function, or spectral density function. Polynomial splines are used to m ..."
Abstract

Cited by 142 (14 self)
 Add to MetaCart
ANOVA type models are considered for a regression function or for the logarithm of a probability function, conditional probability function, density function, conditional density function, hazard function, conditional hazard function, or spectral density function. Polynomial splines are used to model the main effects, and their tensor products are used to model any interaction components that are included. In the special context of survival analysis, the baseline hazard function is modeled and nonproportionality is allowed. In general, the theory involves the L 2 rate of convergence for the fitted model and its components. The methodology involves least squares and maximum likelihood estimation, stepwise addition of basis functions using Rao statistics, stepwise deletion using Wald statistics, and model selection using BIC, crossvalidation or an independent test set. Publically available software, written in C and interfaced to S/SPLUS, is used to apply this methodology to...
ImageBased BRDF Measurement Including Human Skin
"... We present a new imagebased process for measuring the bidirectional reflectance of homogeneous surfaces rapidly, completely, and accurately. For simple sample shapes (spheres and cylinders) the method requires only a digital camera and a stable light source. Adding a 3D scanner allows a wide cla ..."
Abstract

Cited by 115 (2 self)
 Add to MetaCart
We present a new imagebased process for measuring the bidirectional reflectance of homogeneous surfaces rapidly, completely, and accurately. For simple sample shapes (spheres and cylinders) the method requires only a digital camera and a stable light source. Adding a 3D scanner allows a wide class of curved nearconvex objects to be measured. With measurements for a variety of materials from paints to human skin, we demonstrate the new method's ability to achieve high resolution and accuracy over a large domain of illumination and reflection directions. We verify our measurements by tests of internal consistency and by comparison against measurements made using a gonioreflectometer.
KernelBased Reinforcement Learning
 Machine Learning
, 1999
"... We present a kernelbased approach to reinforcement learning that overcomes the stability problems of temporaldifference learning in continuous statespaces. First, our algorithm converges to a unique solution of an approximate Bellman's equation regardless of its initialization values. Second, the ..."
Abstract

Cited by 102 (1 self)
 Add to MetaCart
We present a kernelbased approach to reinforcement learning that overcomes the stability problems of temporaldifference learning in continuous statespaces. First, our algorithm converges to a unique solution of an approximate Bellman's equation regardless of its initialization values. Second, the method is consistent in the sense that the resulting policy converges asymptotically to the optimal policy. Parametric value function estimates such as neural networks do not possess this property. Our kernelbased approach also allows us to show that the limiting distribution of the value function estimate is a Gaussian process. This information is useful in studying the biasvariance tradeo in reinforcement learning. We find that all reinforcement learning approaches to estimating the value function, parametric or nonparametric, are subject to a bias. This bias is typically larger in reinforcement learning than in a comparable regression problem.
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
Abstract

Cited by 92 (17 self)
 Add to MetaCart
Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion function over a sequence of approximating parameter spaces, called sieves, which are significantly less complex than the original parameter space. With different choices of criteria and sieves, the method of sieves is very flexible in estimating complicated econometric models. For example, it can simultaneously estimate the parametric and nonparametric components in seminonparametric models with or without constraints. It can easily incorporate prior information, often derived from economic theory, such as monotonicity, convexity, additivity, multiplicity, exclusion and nonnegativity. This chapter describes estimation of seminonparametric econometric models via the method of sieves. We present some general results on the large sample properties of the sieve estimates, including consistency of the sieve extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Inverse Rendering for Computer Graphics
, 1998
"... Creating realistic images has been a major focus in the study of computer graphics for much of its history. This e ort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geomet ..."
Abstract

Cited by 87 (4 self)
 Add to MetaCart
Creating realistic images has been a major focus in the study of computer graphics for much of its history. This e ort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geometry of objects, the re ectance of surfaces, and the lighting used to illuminate the scene. These images accurately describe the physical quantities that would be measured from a real scene. Because these algorithms can predict real images, they can also be used in inverse problems to work backward from photographs to attributes of the scene. Work on three such inverse rendering problems is described. The rst, inverse lighting, assumes knowledge of geometry, re ectance, and the recorded photograph and solves for the lighting in the scene. A technique using a linear leastsquares system is proposed and demonstrated. Also demonstrated is an application of inverse lighting, called relighting, which modi es lighting in photographs. The second two inverse rendering problems solve for unknown re ectance, given images with known geometry, lighting, and camera positions. Photographic texture measurement
Efficient Estimation of Conditional Variance Functions in Stochastic Regression
 Biometrika
, 1998
"... this paper is to derive an ecient fullyadaptive procedure for estimating ..."
Abstract

Cited by 80 (9 self)
 Add to MetaCart
this paper is to derive an ecient fullyadaptive procedure for estimating
Generalized Likelihood Ratio Statistics And Wilks Phenomenon
, 2000
"... this paper. We introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks phenomenon is unveiled. We demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are ..."
Abstract

Cited by 78 (22 self)
 Add to MetaCart
this paper. We introduce the generalized likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks phenomenon is unveiled. We demonstrate that a class of the generalized likelihood statistics based on some appropriate nonparametric estimators are asymptotically distribution free and follow