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593
Locationaided routing (LAR) in mobile ad hoc networks, in:
 Proc. of MOBICOM
, 1998
"... A mobile ad hoc network consists of wireless hosts that may move often. Movement of hosts results in a change in routes, requiring some mechanism for determining new routes. Several routing protocols have already been proposed for ad hoc networks. This paper suggests an approach to utilize location ..."
Abstract

Cited by 901 (10 self)
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location information (for instance, obtained using the global positioning system) to improve performance of routing protocols for ad hoc networks. By using location information, the proposed LocationAided Routing (LAR) protocols limit the search for a new route to a smaller "request zone
Least angle regression
, 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
Abstract

Cited by 1326 (37 self)
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
An Algorithm for Tracking Multiple Targets
 IEEE Transactions on Automatic Control
, 1979
"... Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m[~clngreports, and ..."
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Cited by 596 (0 self)
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Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m
Regularization and variable selection via the Elastic Net.
 J. R. Stat. Soc. Ser. B
, 2005
"... Abstract We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, wher ..."
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Cited by 973 (11 self)
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. An efficient algorithm called LARSEN is proposed for computing elastic net regularization paths efficiently, much like the LARS algorithm does for the lasso.
A rapid hierarchical radiosity algorithm
 Computer Graphics
, 1991
"... This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a usersupplied error bound. The a ..."
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Cited by 409 (11 self)
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This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a usersupplied error bound
The adaptive nature of human categorization
 Psychological Review
, 1991
"... A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partiti ..."
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Cited by 344 (2 self)
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partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning
Pathwise coordinate optimization
, 2007
"... We consider “oneatatime ” coordinatewise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1penalized regression (lasso) in the lterature, but it seems to have been largely ignored. Indeed, it seems that coordinatewise algorith ..."
Abstract

Cited by 325 (17 self)
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wise algorithms are not often used in convex optimization. We show that this algorithm is very competitive with the well known LARS (or homotopy) procedure in large lasso problems, and that it can be applied to related methods such as the garotte and elastic net. It turns out that coordinatewise descent does
Supervisor at Nada was Lars Kjelldahl
"... Noise based techniques have for a long time been one of the most important tools in the creation of virtual threedimensional environments. For instance, virtually all computer animated movies incorporate these algorithms for a variety of applications. Despite its power and usefulness, noise has bee ..."
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Noise based techniques have for a long time been one of the most important tools in the creation of virtual threedimensional environments. For instance, virtually all computer animated movies incorporate these algorithms for a variety of applications. Despite its power and usefulness, noise has
A Fast Tracking ALgorithm for Generalized LARS/LASSO
, 2006
"... This paper gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the L_1 regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu’s path tracking algorit ..."
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Cited by 5 (1 self)
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This paper gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the L_1 regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu’s path tracking
LARS: A learning algorithm for rewriting systems
, 2007
"... Whereas there is a number of methods and algorithms to learn regular languages, moving up the Chomsky hierarchy is proving to be a challenging task. Indeed, several theoretical barriers make the class of contextfree languages hard to learn. To tackle these barriers, we choose to change the way we r ..."
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candidates in forthcoming applications of grammatical inference. In this paper, we pioneer the problem of their learnability. We propose a novel and sound algorithm (called LARS) which identifies a large subclass of them in polynomial time (but not data). We illustrate the execution of our algorithm through
Results 1  10
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593