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The application of an oblique-projected Landweber method to a model of supervised learning
Björn Johansson
, Tommy Elfving
, Vladimir Kozlov
,
Yair Censor
, Per Erik Forssén
, Gösta Granlund
Department of Mathematics
Research output
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peer-review
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Computer Science
Computer Vision
100%
Constraint Set
50%
Convergence Property
50%
Convex Constraint
50%
Function Approximation
50%
Information Representation
100%
Iterative Algorithm
50%
Least Squares Method
100%
Multilevel Model
50%
Object Pose
50%
Object Recognition
50%
Oblique Projection
50%
Pose Estimation
50%
Representation Model
100%
Signal Processing
50%
Supervised Learning
100%
Transient Signal
50%
Mathematics
Approximation Function
100%
Constrained Least Squares
100%
Convergence Property
100%
Convergence Result
100%
Convex
100%
Learning Task
100%
Level Model
100%
Rate of Convergence
100%
Signal Processing
100%
Weighted Least Squares
100%
Keyphrases
Constrained Weighted Least Squares
33%
Effective Computation
33%