Results 1  10
of
771,448
A WEIGHTED GENERALIZED LS–SVM
, 2003
"... Neural networks play an important role in system modelling. This is especially true if model building is mainly based on observed data. Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they automatically answer certain crucial questio ..."
Abstract
 Add to MetaCart
) sense (LS2 –SVM), a pruned solution is achieved, while the computational burden is further reduced (Generalized LS–SVM). In this generalized LS–SVM framework a further modification weighting is also proposed, to reduce the sensitivity of the network construction to outliers while maintaining sparseness.
Termweighting approaches in automatic text retrieval
 INFORMATION PROCESSING AND MANAGEMENT
, 1988
"... The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucia ..."
Abstract

Cited by 2159 (10 self)
 Add to MetaCart
The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend
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 594 (53 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
Inverse Acoustic and Electromagnetic Scattering Theory, Second Edition
, 1998
"... Abstract. This paper is a survey of the inverse scattering problem for timeharmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newtontype methods for solving the inverse scattering problem for acoustic waves, including a brief discussi ..."
Abstract

Cited by 1072 (45 self)
 Add to MetaCart
Abstract. This paper is a survey of the inverse scattering problem for timeharmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newtontype methods for solving the inverse scattering problem for acoustic waves, including a brief
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
Abstract

Cited by 446 (46 self)
 Add to MetaCart
of equations in the dual space. While the SVM classifier has a large margin interpretation, the LSSVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization
LSSVM Regression Modelling and its Applications
, 2004
"... a class of kernel based learning methods that fits within the penalized modelling paradigm. Primary goals of the LSSVM models are regression and classification. Although local methods (kernel methods) focus directly on estimating the function at a point, they face problems in high dimensions. There ..."
Abstract
 Add to MetaCart
a class of kernel based learning methods that fits within the penalized modelling paradigm. Primary goals of the LSSVM models are regression and classification. Although local methods (kernel methods) focus directly on estimating the function at a point, they face problems in high dimensions
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
"... ..."
Nonlinear component analysis as a kernel eigenvalue problem

, 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
Abstract

Cited by 1554 (85 self)
 Add to MetaCart
possible 5pixel products in 16x16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach; and present first experimental results on nonlinear feature extraction for pattern recognition.
A computational approach to edge detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1986
"... AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal ..."
Abstract

Cited by 4621 (0 self)
 Add to MetaCart
AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal
Results 1  10
of
771,448