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2,333
Kernel Regression Trees
- Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics
"... This paper presents a novel method for learning in domains with continuous target variables. The method integrates regression trees with kernel regression models. The integration is done by adding kernel regressors at the tree leaves producing what we call kernel regression trees. The approach is mo ..."
Abstract
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Cited by 9 (1 self)
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This paper presents a novel method for learning in domains with continuous target variables. The method integrates regression trees with kernel regression models. The integration is done by adding kernel regressors at the tree leaves producing what we call kernel regression trees. The approach
Kernel regression for image processing and reconstruction
- IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2007
"... In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, ..."
Abstract
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Cited by 172 (53 self)
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In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation
Probabilistic Kernel Regression Models
- In Proceedings of the 1999 Conference on AI and Statistics
, 1999
"... We introduce a class of flexible conditional probability models and techniques for classification /regression problems. Many existing methods such as generalized linear models and support vector machines are subsumed under this class. The flexibility of this class of techniques comes from the use of ..."
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Cited by 113 (2 self)
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of kernel functions as in support vector machines, and the generality from dual formulations of standard regression models. 1 Introduction Support vector machines [10] are linear maximum margin classifiers exploiting the idea of a kernel function. A kernel function defines an embedding of examples
Sequential Bayesian Kernel Regression
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2003
"... We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm, ..."
Abstract
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Cited by 9 (2 self)
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We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm,
Metric Learning for Kernel Regression
"... Kernel regression is a well-established method for nonlinear regression in which the target value for a test point is estimated using a weighted average of the surrounding training samples. The weights are typically obtained by applying a distance-based kernel function to each of the samples, which ..."
Abstract
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Cited by 29 (0 self)
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Kernel regression is a well-established method for nonlinear regression in which the target value for a test point is estimated using a weighted average of the surrounding training samples. The weights are typically obtained by applying a distance-based kernel function to each of the samples, which
Regularized Kernel Regression for Image
"... Abstract — The framework of kernel regression [1], a nonparametric estimation method, has been widely used in different guises for solving a variety of image processing problems including denoising and interpolation [2]. In this paper, we extend the use of kernel regression for deblurring applicatio ..."
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Abstract — The framework of kernel regression [1], a nonparametric estimation method, has been widely used in different guises for solving a variety of image processing problems including denoising and interpolation [2]. In this paper, we extend the use of kernel regression for deblurring
Reducing Hubness for Kernel Regression
"... Abstract. In this paper, we point out that hubness—some samples in a high-dimensional dataset emerge as hubs that are similar to many other samples—influences the performance of kernel regression. Because the di-mension of feature spaces induced by kernels is usually very high, hubness occurs, givin ..."
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Abstract. In this paper, we point out that hubness—some samples in a high-dimensional dataset emerge as hubs that are similar to many other samples—influences the performance of kernel regression. Because the di-mension of feature spaces induced by kernels is usually very high, hubness occurs
Evolutionary Unsupervised Kernel Regression
"... Dimension reduction and manifold learning play an important role in robotics, mul-timedia processing and data mining. For these tasks strong methods like Unsupervised Kernel Regression [4, 7] or Gaussian Process Latent Variable Models [5, 6] have been pro-posed in the last years. But many methods su ..."
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Dimension reduction and manifold learning play an important role in robotics, mul-timedia processing and data mining. For these tasks strong methods like Unsupervised Kernel Regression [4, 7] or Gaussian Process Latent Variable Models [5, 6] have been pro-posed in the last years. But many methods
Bias-Corrected Kernel Regression
"... . This paper proposes a simple and practical iterative method for bias-corrected kernel regression. The proposed approach corrects for both curvature-based and boundary-based finite-sample bias. The method is proposed as an alternative to bias-reduction through the estimation of leading terms in a ..."
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Cited by 1 (0 self)
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. This paper proposes a simple and practical iterative method for bias-corrected kernel regression. The proposed approach corrects for both curvature-based and boundary-based finite-sample bias. The method is proposed as an alternative to bias-reduction through the estimation of leading terms
Kernel Regression with Order Preferences∗
"... We propose a novel kernel regression algorithm which takes into account order preferences on unlabeled data. Such preferences have the form that point x1 has a larger target value than that of x2, although the tar-get values for x1, x2 are unknown. The order pref-erences can be viewed as side inform ..."
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Cited by 3 (0 self)
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We propose a novel kernel regression algorithm which takes into account order preferences on unlabeled data. Such preferences have the form that point x1 has a larger target value than that of x2, although the tar-get values for x1, x2 are unknown. The order pref-erences can be viewed as side
Results 1 - 10
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2,333
