Results 1  10
of
130,150
Minimal Kernel Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2002
"... A finite concave minimization algorithm is proposed for constructing kernel classifiers that use a minimal number of data points both in generating and characterizing a classifier. The algorithm ..."
Abstract

Cited by 24 (8 self)
 Add to MetaCart
A finite concave minimization algorithm is proposed for constructing kernel classifiers that use a minimal number of data points both in generating and characterizing a classifier. The algorithm
Active Contours without Edges
, 2001
"... In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, MumfordShah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy ..."
Abstract

Cited by 1206 (38 self)
 Add to MetaCart
of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods
FiniteState Transducers in Language and Speech Processing
 Computational Linguistics
, 1997
"... Finitestate machines have been used in various domains of natural language processing. We consider here the use of a type of transducers that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential stringtostring transducer ..."
Abstract

Cited by 392 (42 self)
 Add to MetaCart
tostring transducers. Transducers that output weights also play an important role in language and speech processing. We give a specific study of stringtoweight transducers, including algorithms for determinizing and minimizing these transducers very efficiently, and characterizations of the transducers admitting
Clustering via Concave Minimization
 Advances in Neural Information Processing Systems 9
, 1997
"... The problem of assigning m points in the ndimensional real space R n to k clusters is formulated as that of determining k centers in R n such that the sum of distances of each point to the nearest center is minimized. If a polyhedral distance is used, the problem can be formulated as that of ..."
Abstract

Cited by 65 (17 self)
 Add to MetaCart
as that of minimizing a piecewiselinear concave function on a polyhedral set which is shown to be equivalent to a bilinear program: minimizing a bilinear function on a polyhedral set. A fast finite kMedian Algorithm consisting of solving few linear programs in closed form leads to a stationary point of the bilinear
Iterative Methods For Total Variation Denoising
 SIAM J. SCI. COMPUT
"... Total Variation (TV) methods are very effective for recovering "blocky", possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TVpenalized least squares functional is presented and compared with existing minimization schemes. A variant of the cellcenter ..."
Abstract

Cited by 341 (7 self)
 Add to MetaCart
Total Variation (TV) methods are very effective for recovering "blocky", possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TVpenalized least squares functional is presented and compared with existing minimization schemes. A variant of the cell
Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 1992
"... Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root. This p ..."
Abstract

Cited by 318 (14 self)
 Add to MetaCart
Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root
A finitevolume, incompressible Navierâ€“Stokes model for studies of the ocean on parallel computers.
 J. Geophys. Res.,
, 1997
"... Abstract. The numerical implementation of an ocean model based on the incompressible Navier Stokes equations which is designed for studies of the ocean circulation on horizontal scales less than the depth of the ocean right up to global scale is described. A "pressure correction" method i ..."
Abstract

Cited by 293 (32 self)
 Add to MetaCart
gradient iteration is used to invert symmetric elliptic operators in both two and three dimensions. Physically motivated preconditioners are designed which are efficient at reducing computation and minimizing communication between processors. Our method exploits the fact that as the horizontal scale of the motion
A New Finite Cone Covering Algorithm for Concave Minimization
, 1998
"... We propose a new finite cone covering algorithm for concave minimization over a polytope, in which the cones are defined by extreme points of the polytope. The main novelties are the use of cones defined by an arbitrary number of edges, and the subdivision process. This latter is shown to have a &qu ..."
Abstract
 Add to MetaCart
We propose a new finite cone covering algorithm for concave minimization over a polytope, in which the cones are defined by extreme points of the polytope. The main novelties are the use of cones defined by an arbitrary number of edges, and the subdivision process. This latter is shown to have a
A new alternating minimization algorithm for total variation image reconstruction
 SIAM J. IMAGING SCI
, 2008
"... We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new halfquadratic model applicable to not only the anisotropic but also isotropic forms of total variati ..."
Abstract

Cited by 224 (26 self)
 Add to MetaCart
We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new halfquadratic model applicable to not only the anisotropic but also isotropic forms of total
Results 1  10
of
130,150