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TNN
2008
82views more  TNN 2008»
15 years 6 months ago
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find ...
Cristiano Cervellera, Danilo Macciò, Marco ...
CORR
2012
Springer
235views Education» more  CORR 2012»
14 years 2 months ago
An Incremental Sampling-based Algorithm for Stochastic Optimal Control
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
Vu Anh Huynh, Sertac Karaman, Emilio Frazzoli
MICCAI
2003
Springer
16 years 7 months ago
The Euler-Lagrange Equation for Interpolating Sequence of Landmark Datasets
Abstract. Non-rigid registration of landmarked datasets is an important problem that finds many applications in medical image analysis. In this paper, we present a method for inter...
Alain Trouvé, Laurent Younes, Michael I. Mi...
AI
1998
Springer
15 years 11 months ago
A Heuristic Incremental Modeling Approach to Course Timetabling
Abstract. The general timetabling problem is an assignment of activities to xed time intervals, adhering to a prede ned set of resource availabilities. Timetabling problems are di ...
Don Banks, Peter van Beek, Amnon Meisels
CORR
2010
Springer
168views Education» more  CORR 2010»
15 years 6 months ago
Penalty Decomposition Methods for Rank Minimization
In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first show that a class of matrix optimization prob...
Zhaosong Lu, Yong Zhang