Results 1  10
of
2,559
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 476 (46 self)
 Add to MetaCart
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
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
 Machine Learning,
, 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
Abstract

Cited by 707 (2 self)
 Add to MetaCart
in the average tree size in AdaBoost trials and its success in reducing the error. We compare the meansquared error of voting methods to nonvoting methods and show that the voting methods lead to large and significant reductions in the meansquared errors. Practical problems that arise in implementing boosting
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
Abstract

Cited by 578 (16 self)
 Add to MetaCart
algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely
The Kernel Recursive Least Squares Algorithm
 IEEE Transactions on Signal Processing
, 2003
"... We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS (KRLS) algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared error regressor. Spars ..."
Abstract

Cited by 141 (2 self)
 Add to MetaCart
We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS (KRLS) algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared error regressor
Concept Decompositions for Large Sparse Text Data using Clustering
 Machine Learning
, 2000
"... . Unlabeled document collections are becoming increasingly common and available; mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as highdimensional and sparse vectorsa few thousand dimensions and a sparsity of 95 to 99 ..."
Abstract

Cited by 407 (27 self)
 Add to MetaCart
to 99% is typical. In this paper, we study a certain spherical kmeans algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we
Mixture Kernel Least Mean Square
"... Abstract—Instead of using single kernel, different approaches of using multiple kernels have been proposed recently in kernel learning literature, one of which is multiple kernel learning (MKL). In this paper, we propose an alternative to MKL in order to select the appropriate kernel given a pool of ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
mixture of models. We propose mixture kernel least mean square (MxKLMS) adaptive filtering algorithm, where the kernel least mean square (KLMS) filters learned with different kernels, act in parallel at each input instance and are competitively combined such that the filter with the best kernel
The Kernel Recursive LeastSquares Algorithm
"... Abstract—We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a highdimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum meansquarederror solutions to nonlinear leasts ..."
Abstract
 Add to MetaCart
Abstract—We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a highdimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum meansquarederror solutions to nonlinear leastsquares
Kernel partial least squares regression in reproducing kernel Hilbert space
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the late ..."
Abstract

Cited by 154 (10 self)
 Add to MetaCart
A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables
1Bayesian Extensions of Kernel Least Mean Squares
"... Abstract—The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that “kernelizes ” the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering, and thu ..."
Abstract
 Add to MetaCart
Abstract—The kernel least mean squares (KLMS) algorithm is a computationally efficient nonlinear adaptive filtering method that “kernelizes ” the celebrated (linear) least mean squares algorithm. We demonstrate that the least mean squares algorithm is closely related to the Kalman filtering
A LeastSquares Approach to Blind Channel Identification
 IEEE Trans. Signal Processing
, 1995
"... Conventional blind channel idenffiication algorithm.q are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates ..."
Abstract

Cited by 183 (7 self)
 Add to MetaCart
Conventional blind channel idenffiication algorithm.q are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough
Results 1  10
of
2,559