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Support Vector Machines,Kernel Logistic Regression,and Boosting
Support Vector Machines Kernel Logistic Regression Boosting
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2015/8/21
Support Vector Machines,Kernel Logistic Regression,and Boosting.
Iterative Water-Filling for Gaussian Vector Multiple Access Channels
channel capacity convex optimization Gaussian channels multiuser channels multiple-access channels
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2015/7/10
We develop an efficient iterative water-filling algorithm to find an optimal transmit spectrum for maximum sum capacity in a Gaussian multiple access channel with vector inputs and a vector output. Th...
The Mean Number-in-System Vector Range for a Multi-Class Queueing Network
networks of servers surjections onto multifunctions queueing
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2015/7/6
In a multiclass network of queues, natural sufficient conditions are given for the mean number-in-system vector to cover the nonnegative orthant as the arrival rate vector varies over the stable domai...
Discrete vector on-site vortices
Discrete vortex coupling discrete nonlinear schrodinger equation crystal lattice coupling
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2014/12/25
We study discrete vortices in coupled discrete nonlinear Schrödinger equations. We focus on the vortex cross configuration that has been experimentally observed in photorefractive crystals. Stabi...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
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2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
Independent Vector Analysis: Identification Conditions and Performance Bounds
Independent Vector Analysis Identification Conditions Performance Bounds
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2013/5/2
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research interest. IVA has a...
On confidence intervals in regression that utilize uncertain prior information about a vector parameter
Frequentist confidence interval Prior information Linear regression
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2013/4/28
Consider a linear regression model with n-dimensional response vector, p-dimensional regression parameter beta and independent normally distributed errors. Suppose that the parameter of interest is th...
Complex Support Vector Machines for Regression and Quaternary Classification
Support Vector Machines Kernel methods Widely linear estimation com-plex data
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2013/4/28
We present a support vector machines (SVM) rationale suitable for regression and quaternary classification problems that use complex data, exploiting the notions of widely linear estimation and pure c...
An Equivalence between the Lasso and Support Vector Machines
Equivalence the Lasso Support Vector Machines
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2013/4/28
We investigate the relation of two fundamental tools in machine learning, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resu...
Singular Vector Perturbation under Gaussian Noise
Singular Vector Perturbation Gaussian Noise
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2012/9/18
We study the following problem: when a low rank matrix is perturbed by Gaus-sian noise, what is the distribution of the induced (by singular value decompostion) perturbation on its singular vectors? I...
Sparse Vector Autoregressive Modeling
vector autoregressive (VAR) model sparsity partial spectral coherence (PSC) model selection.
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2012/9/18
The vector autoregressive (VAR) model has been widely used for modeling temporal de-pendence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can ...
BSVM: A Banded Suport Vector Machine
BSVM Banded Suport Vector Machine
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2011/7/19
We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \in...
Kernels for Vector-Valued Functions: a Review
Kernels for Vector-Valued Functions: a Review
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2011/7/6
Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choi...
Large Vector Auto Regressions
Time Series Vector Auto Regression Regularization Lasso Group Lasso
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2011/7/6
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for...
Vector Diffusion Maps and the Connection Laplacian
Dimensionality reduction vector elds heat kernel parallel transport local prin-cipal component analysis alignment
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2011/3/18
Abstract. We introduce vector di usion maps (VDM), a new mathematical framework for orga-nizing and analyzing massive high dimensional data sets, images and shapes. VDM is a mathematical and algorithm...