- Referenced in 92 articles
- others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete...
- Referenced in 60 articles
- smooth support vector machine for classification. Smoothing methods, extensively used for solving important mathematical programming ... pattern classification using a completely arbitrary kernel. We term such reformulation a Smooth Support Vector ... light [T. Joachims, in: Advances in kernel methods – support vector learning, MIT Press: Cambridge ... Platt, in: Advances in kernel methods – support vector learning, MIT Press: Cambridge, MA (1999)]. SSVM...
- Referenced in 67 articles
- Smola (eds.), Advances in kernel methods. London: MIT Press (1998; Zbl 0935.68084)] for classification problems...
- Referenced in 106 articles
- Library. This class of methods uses quite different computational kernels than the traditional simplex method...
- Referenced in 89 articles
- Kernel-based Machine Learning Lab. Kernel-based machine learning methods for classification, regression, clustering, novelty ... reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes...
- Referenced in 77 articles
- content for the new book Kernel-Based Approximation Methods in MATLAB available from World Scientific...
- Referenced in 30 articles
- Kernel Methods Matlab Toolbox. Key Features: SVM Classification using linear and quadratic penalization of misclassified ... Networks; SVM bounds (Span estimate, radius/margin); Wavelet Kernel; SVM Based Feature Selection; Kernel PCA; Kernel...
- Referenced in 23 articles
- page is devoted to learning methods building on kernels, such as the support vector machine ... here. In those days, information about kernel methods was sparse and nontrivial to find...
- Referenced in 94 articles
- contrast, previous analyses of stochastic gradient descent methods for SVMs require Ω(1/ϵ2) iterations ... linear kernel, the total run-time of our method is O (d/(λϵ)) , where ... approach also extends to non-linear kernels while working solely on the primal objective function ... magnitude speedup over previous SVM learning methods...
- Referenced in 18 articles
- nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models ... free data to the deconvolution kernel estimation. Several methods for the selection of the data...
- Referenced in 14 articles
- nonparametric density estimation. Operationally, the kernel method is used throughout to estimate the density. Diagnostics ... methods for evaluating the quality of the clustering are available. The package includes also ... probability density function obtained by the kernel method, given a set of data with arbitrary...
- Referenced in 26 articles
- many other recent developments in kernel based methods in general. Originally, it has been introduced ... theory and structural risk minimization. In the methods one solves convex optimization problems, typically quadratic ... exploit primal-dual interpretations. Links between kernel versions of classical pattern recognition algorithms such ... kernel PLS. Recent developments are in kernel spectral clustering, data visualization and dimensionality reduction...
- Referenced in 12 articles
- combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates ... inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging ... support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic...
- Referenced in 12 articles
- interactions using the Fast Multipole Method. This is a kernel independent fast multipole method based ... Black-Box method which is an independent kernel formulation (introduced by E. Darve @ Stanford). With ... oscillatory kernels in our library. For the classical method, two approaches are used to decrease ... octree and the parallelization of the method; the kernels. This new architecture allow...
- Referenced in 10 articles
- library for high-dimensional kernel summations. Kernel-based methods are a powerful tool ... bottleneck in these methods is computations involving the kernel matrix, which scales quadratically with ... Kernel Independent Treecode), an efficient, scalable, kernel-independent method for approximately evaluating kernel matrix-vector ... novel, randomized method for efficiently factoring off-diagonal blocks of the kernel matrix using approximate...
- Referenced in 131 articles
- spatial windows, pixel images and tessellations. Exploratory methods include K-functions, nearest neighbour distance ... space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross...
- Referenced in 15 articles
- design of adaptive systems. It comprises methods for single- and multi-objective optimization (e.g., evolutionary ... gradient-based algorithms) as well as kernel-based methods, neural networks, and other machine learning...
- Referenced in 5 articles
- package kequate: The Kernel Method of Test Equating. Performing the Kernel Method of Test Equating ... obtain a fair test. Recently, the kernel method of test equating, which is a conjoint ... test equating, has gained popularity. The kernel method of test equating includes five steps ... covariates. An R package for the kernel method of test equating called kequate is presented...
- Referenced in 69 articles
- package sm: Smoothing methods for nonparametric regression and density estimation. This is software linked ... Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations’ Oxford University...
- Referenced in 65 articles
- modular software in which each statistical method (symbolic objects data base, distance matrix for symbolic ... objects, divisible classification of symbolic data, symbolic kernel discriminant analysis, symbolic description of groups, factorial ... like a chain with links the statistical methods. The top icon represents the symbolic data...