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ICPR
2008
IEEE
14 years 1 months ago
Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
Djamel Bouchaffra
ICRA
2002
IEEE
104views Robotics» more  ICRA 2002»
14 years 13 days ago
Improbability Filtering for Rejecting False Positives
—In this paper we describe a novel approach, called improbability filtering, to rejecting false-positive observations from degrading the tracking performance of an Extended Kalma...
Brett Browning, Michael H. Bowling, Manuela M. Vel...
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
14 years 1 months ago
Learning noise
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Michael D. Schmidt, Hod Lipson
ICUMT
2009
13 years 5 months ago
A new approach to the design of wireless data broadcasting systems: An analysis-based cost-effective scheme
Abstract--A new approach to the design of wireless data broadcasting systems is introduced. The proposed approach is based on the mathematical analysis of the aforementioned system...
Christos Liaskos, Sophia G. Petridou, Georgios I. ...
SIAMSC
2011
151views more  SIAMSC 2011»
13 years 2 months ago
Inexact Newton Methods with Restricted Additive Schwarz Based Nonlinear Elimination for Problems with High Local Nonlinearity
The classical inexact Newton algorithm is an efficient and popular technique for solving large sparse nonlinear system of equations. When the nonlinearities in the system are wellb...
Xiao-Chuan Cai, Xuefeng Li