Results 1 
7 of
7
Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV
, 1998
"... this paper we very briefly review some of these results. RKHS can be chosen tailored to the problem at hand in many ways, and we review a few of them, including radial basis function and smoothing spline ANOVA spaces. Girosi (1997), Smola and Scholkopf (1997), Scholkopf et al (1997) and others have ..."
Abstract

Cited by 150 (11 self)
 Add to MetaCart
this paper we very briefly review some of these results. RKHS can be chosen tailored to the problem at hand in many ways, and we review a few of them, including radial basis function and smoothing spline ANOVA spaces. Girosi (1997), Smola and Scholkopf (1997), Scholkopf et al (1997) and others have noted the relationship between SVM's and penalty methods as used in the statistical theory of nonparametric regression. In Section 1.2 we elaborate on this, and show how replacing the likelihood functional of the logit (log odds ratio) in penalized likelihood methods for Bernoulli [yesno] data, with certain other functionals of the logit (to be called SVM functionals) results in several of the SVM's that are of modern research interest. The SVM functionals we consider more closely resemble a "goodnessoffit" measured by classification error than a "goodnessoffit" measured by the comparative KullbackLiebler distance, which is frequently associated with likelihood functionals. This observation is not new or profound, but it is hoped that the discussion here will help to bridge the conceptual gap between classical nonparametric regression via penalized likelihood methods, and SVM's in RKHS. Furthermore, since SVM's can be expected to provide more compact representations of the desired classification boundaries than boundaries based on estimating the logit by penalized likelihood methods, they have potential as a prescreening or model selection tool in sifting through many variables or regions of attribute space to find influential quantities, even when the ultimate goal is not classification, but to understand how the logit varies as the important variables change throughout their range. This is potentially applicable to the variable/model selection problem in demographic m...
An InformationTheoretic Approach to Traffic Matrix Estimation
 In Proc. ACM SIGCOMM
, 2003
"... Traffic matrices are required inputs for many IP network management ..."
Abstract

Cited by 121 (13 self)
 Add to MetaCart
Traffic matrices are required inputs for many IP network management
Robot Motion Specification: A VisionBased Approach
 Surveys on Mathematics for Industry
, 1995
"... This paper presents an easytouse method for the specification of complex robot motions. The user demonstrates a desired motion by moving an object to be manipulated with his own hand. His performance is measured by a stereo vision system. The optimal motion is reconstructed with the help of a non ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
This paper presents an easytouse method for the specification of complex robot motions. The user demonstrates a desired motion by moving an object to be manipulated with his own hand. His performance is measured by a stereo vision system. The optimal motion is reconstructed with the help of a nonparametric regression technique which is based on smoothing vector splines and crossvalidation. Uncertainties contained in the measurements are considered explicitly in the process of reconstruction. The proposed method can account for different kinds of restrictions imposed on the robot's motion.
Variable Fusion: A New Adaptive Signal Regression Method
, 1997
"... Signal and image processing are active areas of research in both statistics and engineering. Most of this research has emphasized the reconstruction of a "true" underlying pattern from one measured with noise. Our research has a different goal: recognition or prediction of an ancillary quantity y ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Signal and image processing are active areas of research in both statistics and engineering. Most of this research has emphasized the reconstruction of a "true" underlying pattern from one measured with noise. Our research has a different goal: recognition or prediction of an ancillary quantity y associated with each observed pattern x(t). We propose a nonlinear regularized regression technique, variable fusion. Variable fusion produces models of a simple parsimonious form, readily explained to the nonstatistician and possibly affording savings in data collection. In addition, variable fusion models perform well in terms of prediction. In this paper we assume that the quantity y is real and singlevalued and the pattern x(t) is a "signal", i.e., the space of index values t is onedimensional, although we describe the generalization of the method to a multidimensional index space. We use the patterns as the predictors of y. The patterns generally originate as analog signals an...
A Bayesian approach to hybrid splines nonparametric regression
"... A Bayesian approach is considered to estimate the number of basis functions and the smoothing parameter of the hybrid splines nonparametric regression procedure. The method used to obtain the estimate of the regression curve and its Bayesian con dence intervals is based on the reversible jump M ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
A Bayesian approach is considered to estimate the number of basis functions and the smoothing parameter of the hybrid splines nonparametric regression procedure. The method used to obtain the estimate of the regression curve and its Bayesian con dence intervals is based on the reversible jump MCMC (Green1995). Illustrations with simulated data are provided and show good performance of the proposed approach over the existing methods.
Purpose Purpose and Description
"... Key Words and Phrases: illposed problems; partial thin plate smoothing splines; penalized likelihood; semiparametric models; ridge regression; thin plate smoothing splines; truncated singular value decomposition. ..."
Abstract
 Add to MetaCart
Key Words and Phrases: illposed problems; partial thin plate smoothing splines; penalized likelihood; semiparametric models; ridge regression; thin plate smoothing splines; truncated singular value decomposition.
Remote sensing of atmospheric temperature profiles by TIROS Operational
, 1985
"... rSt3 72 no.45 ..."