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Minimum energy mobile wireless networks
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1999
"... We describe a distributed positionbased network protocol optimized for minimum energy consumption in mobile wireless networks that support peertopeer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each n ..."
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Cited by 751 (0 self)
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We describe a distributed positionbased network protocol optimized for minimum energy consumption in mobile wireless networks that support peertopeer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each
Minimum Error Rate Training in Statistical Machine Translation
, 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
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Cited by 663 (7 self)
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Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality.
On the topologies of local minimum spanning trees
 IN PROCEEDINGS OF THE 3RD WORKSHOP ON COMBINATORIAL AND ALGORITHMIC ASPECTS OF NETWORKING (CAAN’06
, 2006
"... This paper is devoted to study the combinatorial properties of Local Minimum Spanning Trees (LMSTs), a geometric structure that is attracting increasing research interest in the wireless sensor networks community. Namely, we study which topologies are allowed for a sensor network that uses, for supp ..."
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Cited by 1 (0 self)
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This paper is devoted to study the combinatorial properties of Local Minimum Spanning Trees (LMSTs), a geometric structure that is attracting increasing research interest in the wireless sensor networks community. Namely, we study which topologies are allowed for a sensor network that uses
Localized MinimumEnergy Broadcasting in AdHoc Networks
, 2003
"... In the minimum energy broadcasting problem, each node can adjust its transmission power in order to minimize total energy consumption but still enable a message originated from a source node to reach all the other nodes in an adhoc wireless network. In all existing solutions each node requires glob ..."
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Cited by 124 (6 self)
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In the minimum energy broadcasting problem, each node can adjust its transmission power in order to minimize total energy consumption but still enable a message originated from a source node to reach all the other nodes in an adhoc wireless network. In all existing solutions each node requires
Subgoal Chaining and the Local Minimum Problem
 IEEE International Joint Conference on Neural Networks
, 1999
"... It is well known that performing gradient descent on fixed surfaces may result in poor travel through getting stuck in local minima and other surface features. Subgoal chaining in supervised learning is a method to improve travel for neural networks by directing local variation in the surface during ..."
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Cited by 3 (1 self)
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during training. This paper shows however that linear subgoal chains such as those used in ERA are not sufficient to overcome the local minimum problem and examines nonlinear subgoal chains as a possible alternative. 1 Introduction A problem long recognised as important for gradient descent techniques
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Solving the Potential Field Local Minimum Problem
"... local minimum problem using internal agent states. Robots and Autonomous Systems, 56 (12). pp. ..."
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local minimum problem using internal agent states. Robots and Autonomous Systems, 56 (12). pp.
GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
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Cited by 637 (79 self)
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GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search
TILT STABILITY OF A LOCAL MINIMUM
 SIAM J. OPTIMIZATION
"... The behavior of a minimizing point when an objective function is tilted by adding a small linear term is studied from the perspective of secondorder conditions for local optimality. The classical condition of a positivedefinite Hessian in smooth problems without constraints is found to have an exa ..."
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Cited by 22 (2 self)
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The behavior of a minimizing point when an objective function is tilted by adding a small linear term is studied from the perspective of secondorder conditions for local optimality. The classical condition of a positivedefinite Hessian in smooth problems without constraints is found to have
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
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Cited by 778 (21 self)
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We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum
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