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Penalty ∫ Term

by Biswadip Dey, Choosing A Regularization Parameter (ocv
"... Given a time series of observed positions {ri} N i=0 objective is to generate a smooth trajectory to fit these data points. in three dimensional space, our primary A penalty term is introduced to assure smoothness of the reconstructed trajectory. N∑ ‖r(ti) − ri ‖ 2 ..."
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Given a time series of observed positions {ri} N i=0 objective is to generate a smooth trajectory to fit these data points. in three dimensional space, our primary A penalty term is introduced to assure smoothness of the reconstructed trajectory. N∑ ‖r(ti) − ri ‖ 2

Representation of the penalty term of

by Freddy Delbaen, Shige Peng, Emanuela Rosazza Gianin , 2008
"... ar ..."
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Representation of the penalty term of dynamic concave utilities

by Freddy Delbaen, Shige Peng, Emanuela Rosazza Gianin , 2008
"... In the context of a Brownian filtration and with a fixed finite time horizon, we will provide a representation of the penalty term of general dynamic concave utilities (hence of dynamic convex risk measures) by applying the theory of g-expectations. 1 ..."
Abstract - Cited by 32 (2 self) - Add to MetaCart
In the context of a Brownian filtration and with a fixed finite time horizon, we will provide a representation of the penalty term of general dynamic concave utilities (hence of dynamic convex risk measures) by applying the theory of g-expectations. 1

Second-order Learning Algorithm with Squared Penalty Term

by Kazumi Saito, Ryohei Nakano
"... This paper compares three penalty terms with respect to the efficiency of supervised learning, by using first- and second-order learning algorithms. Our experiments showed that for a reasonably adequate penalty factor, the combination of the squared penalty term and the second-order learning algorit ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
This paper compares three penalty terms with respect to the efficiency of supervised learning, by using first- and second-order learning algorithms. Our experiments showed that for a reasonably adequate penalty factor, the combination of the squared penalty term and the second-order learning

Evaluation of a Rigidity Penalty Term for Nonrigid Registration

by Marius Staring, Stefan Klein, Josien P. W. Pluim
"... Nonrigid registration of medical images usually does not model properties of different tissue types. This results for example in nonrigid deformations of structures that are rigid. In this work we address this problem by employ-ing a local rigidity penalty term. We illustrate this approach on a 2D s ..."
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Nonrigid registration of medical images usually does not model properties of different tissue types. This results for example in nonrigid deformations of structures that are rigid. In this work we address this problem by employ-ing a local rigidity penalty term. We illustrate this approach on a 2D

Robust utility maximization with an entropic penalty term: Stochastic control and BSDE methods

by Supervisor Prof, Dr. Martin Schweizer, Candidate Giuliana Bordigoni, Candidate Giuliana Bordigoni, Supervisor Prof, Dr. Martin Schweizer , 2005
"... Robust utility maximization with an entropic penalty term: Stochastic control and BSDE methods ..."
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Robust utility maximization with an entropic penalty term: Stochastic control and BSDE methods

Sparse regularization with l q penalty term

by Markus Grasmair, Markus Haltmeier, Otmar Scherzer - Inverse Probl , 2008
"... We consider the stable approximation of sparse solutions to non-linear operator equations by means of Tikhonov regularization with a subquadratic penalty term. Imposing certain assumptions, which for a linear operator are equivalent to the standard range condition, we derive the usual convergence ra ..."
Abstract - Cited by 9 (1 self) - Add to MetaCart
We consider the stable approximation of sparse solutions to non-linear operator equations by means of Tikhonov regularization with a subquadratic penalty term. Imposing certain assumptions, which for a linear operator are equivalent to the standard range condition, we derive the usual convergence

An Optimal Preconditioner for a Class of Saddle Point Problems with a Penalty Term

by Axel Klawonn - SIAM J. Sci. Comput , 1995
"... . Iterative methods are considered for a class of saddle point problems with a penalty term arising from finite element discretizations of certain elliptic problems. An optimal preconditioner which is independent of the discretization and the penalty parameter is constructed. This approach is then u ..."
Abstract - Cited by 43 (4 self) - Add to MetaCart
. Iterative methods are considered for a class of saddle point problems with a penalty term arising from finite element discretizations of certain elliptic problems. An optimal preconditioner which is independent of the discretization and the penalty parameter is constructed. This approach

Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term

by Boaz Nadler, Leonid (aryeh Kontorovich
"... Abstract—Detection of the number of sinusoids embedded in noise is a fundamental problem in statistical signal processing. Most parametric methods minimize the sum of a data fit (likelihood) term and a complexity penalty term. The latter is often derived via information theoretic criteria, such as m ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Abstract—Detection of the number of sinusoids embedded in noise is a fundamental problem in statistical signal processing. Most parametric methods minimize the sum of a data fit (likelihood) term and a complexity penalty term. The latter is often derived via information theoretic criteria

Use of penalty terms in gradientbased iterative reconstruction schemes for optical tomography

by Andreas H Hielscher , Sebastian Bartel - J. Biomed. Opt
"... Abstract. It is well known that the reconstruction problem in optical tomography is ill-posed. In other words, many different spatial distributions of optical properties inside the medium can lead to the same detector readings on the surface of the medium under consideration. Therefore, the choice ..."
Abstract - Cited by 12 (3 self) - Add to MetaCart
of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain a priori information about the system. For the efficient minimization of this objective function
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