Add to ORKGCaption title.Includes bibliographical references (p. 15-16).Supported by the NSF. 9300494-DMIby Dimitri P. Bertsekas
This paper provides a proof of global convergence of gradient search for low-rank matrix approximati...
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dyna...
AbstractIn this paper the development, convergence theory and numerical testing of a class of gradie...
Caption title.Includes bibliographical references (p. 13-14).Supported by the NSF. 9300494-DMIby Dim...
Abstract. The least mean squares (LMS) method for linear least squares problems differs from the ste...
Caption titleIncludes bibliographical references (leaves 16-18).Supported by the NSF. 9300494-DMIby ...
Recently a number of publications have proposed alternative methods to apply in least mean square (L...
Many recent problems in signal processing and machine learning such as compressed sensing, image res...
We consider the class of incremental gradient methods for minimizing a sum of continuously different...
In this paper we describe an on-line method of training neural networks which is based on solving th...
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. ...
This report is a complement to the working document [1], where a sparse associative network is descr...
When minimizing a nonlinear least-squares function, the Levenberg-Marquardt algorithm can suffer fro...
In the modern digital economy, optimal decision support systems, as well as machine learning systems...
Interleaved learning in machine learning algorithms is a biologically inspired training method with ...
This paper provides a proof of global convergence of gradient search for low-rank matrix approximati...
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dyna...
AbstractIn this paper the development, convergence theory and numerical testing of a class of gradie...
Caption title.Includes bibliographical references (p. 13-14).Supported by the NSF. 9300494-DMIby Dim...
Abstract. The least mean squares (LMS) method for linear least squares problems differs from the ste...
Caption titleIncludes bibliographical references (leaves 16-18).Supported by the NSF. 9300494-DMIby ...
Recently a number of publications have proposed alternative methods to apply in least mean square (L...
Many recent problems in signal processing and machine learning such as compressed sensing, image res...
We consider the class of incremental gradient methods for minimizing a sum of continuously different...
In this paper we describe an on-line method of training neural networks which is based on solving th...
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. ...
This report is a complement to the working document [1], where a sparse associative network is descr...
When minimizing a nonlinear least-squares function, the Levenberg-Marquardt algorithm can suffer fro...
In the modern digital economy, optimal decision support systems, as well as machine learning systems...
Interleaved learning in machine learning algorithms is a biologically inspired training method with ...
This paper provides a proof of global convergence of gradient search for low-rank matrix approximati...
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dyna...
AbstractIn this paper the development, convergence theory and numerical testing of a class of gradie...