The Experts below are selected from a list of 15042 Experts worldwide ranked by ideXlab platform

Li Jie-hong - One of the best experts on this subject based on the ideXlab platform.

Suresh Chandra - One of the best experts on this subject based on the ideXlab platform.

  • Generalized Eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives
    International Journal of Machine Learning and Cybernetics, 2018
    Co-Authors: Reshma Khemchandani, Keshav Goyal, Suresh Chandra
    Abstract:

    Generalized Eigenvalue proximal support vector regressor (GEPSVR) determines a pair of \(\epsilon\)-insensitive bounding regressors by solving a pair of Generalized Eigenvalue problem. On the lines of GEPSVR, in this paper we propose a novel regressor for the simultaneous learning of a function and its derivatives, termed as GEPSVR of a Function and its Derivatives. The proposed method is fast as it requires the solution of a pair of Generalized Eigenvalue problems as compared to the solution of a large Quadratic Programming Problem required in other existing approaches. The experiment results on several benchmark functions of more than one variable proves the efficacy of our proposed method.

  • Generalized Eigenvalue proximal support vector regressor
    Expert Systems With Applications, 2011
    Co-Authors: Reshma Khemchandani, Anuj Karpatne, Suresh Chandra
    Abstract:

    In this paper, we propose a new non-parallel plane based regressor termed as Generalized Eigenvalue Proximal Support Vector Regressor (GEPSVR). The GEPSVR formulation is in the spirit of non-parallel plane proximal SVMs via Generalized Eigenvalues and is obtained by solving two Generalized Eigenvalue problems. Further, an improvement over GEPSVR is proposed that employs a regularization technique, similar to the one proposed in Guarracino, Cifarelli, Seref, and Pardalos (2007), which requires the solution of a single regularized Eigenvalue problem only. This regressor has been termed as Regularized GEPSVR (ReGEPSVR). On several benchmark datasets and artificially generated datasets, ReGEPSVR is not only fast, but also shows good generalization when compared with other regression algorithms. It also finds its application in financial time-series forecasting, as shown over financial datasets.

Ying Tang - One of the best experts on this subject based on the ideXlab platform.

  • notes on recurrent neural network model for computing largest and smallest Generalized Eigenvalue
    Neurocomputing, 2010
    Co-Authors: Ying Tang
    Abstract:

    We show some mistakes in the paper ''Recurrent neural network model for computing largest and smallest Generalized Eigenvalue, Neurocomputing 71 (2008) 3589-3594'' using a counterexample. And another recurrent neural network (RNN) with invariant B-norm is proposed for computing the largest or smallest Generalized Eigenvalue and the corresponding eigenvector of any symmetric positive pair (A,B), which can be simply extended to compute the second largest or smallest Generalized Eigenvalue and the corresponding eigenvector based on the similar skills established in other literature. In addition, convergence of such RNN is proven rigorously. Simulation results demonstrate the computational capability of such model.

Dong Nan - One of the best experts on this subject based on the ideXlab platform.

  • letters recurrent neural network model for computing largest and smallest Generalized Eigenvalue
    Neurocomputing, 2008
    Co-Authors: Lijun Liu, Hongmei Shao, Dong Nan
    Abstract:

    A continuous recurrent neural network model is presented for computing the largest and smallest Generalized Eigenvalue of a symmetric positive pair (A,B). Convergence properties to the extremum Eigenvalues based upon Liapunov functional with the help of the Generalized eigen-decomposition theorem is obtained. Compared with other existing models, this model is also suitable for computing the smallest Generalized Eigenvalue simply by replacing A by -A as well as maintaining invariant norm property. Numerical simulation further shows the effectiveness of the proposed model.

Umpei Nagashima - One of the best experts on this subject based on the ideXlab platform.

  • a filter diagonalization for Generalized Eigenvalue problems based on the sakurai sugiura projection method
    Journal of Computational and Applied Mathematics, 2010
    Co-Authors: Tsutomu Ikegami, Tetsuya Sakurai, Umpei Nagashima
    Abstract:

    The Sakurai-Sugiura projection method, which solves Generalized Eigenvalue problems to find certain Eigenvalues in a given domain, was reformulated by using the resolvent theory. A new interpretation based on filter diagonalization was given, and the corresponding filter function was derived explicitly. A block version of the method was also proposed, which enabled not only resolution of degenerated Eigenvalues, but also an improvement in numerical accuracy. Three numerical examples were provided to illustrate the method.

  • a parallel method for large sparse Generalized Eigenvalue problems using a gridrpc system
    Future Generation Computer Systems, 2008
    Co-Authors: Tetsuya Sakurai, Yoshihisa Kodaki, Hiroto Tadano, Daisuke Takahashi, Mitsuhisa Sato, Umpei Nagashima
    Abstract:

    In this paper we present a master-worker type parallel method for finding several Eigenvalues and eigenvectors of a Generalized Eigenvalue problem [email protected], where A and B are large sparse matrices. A moment-based method that finds all of the Eigenvalues that lie inside a given domain is used. In this method, a small matrix pencil that has only the desired Eigenvalues is derived by solving large sparse systems of linear equations constructed from A and B. Since these equations can be solved independently, we solve them on remote servers in parallel. This approach is suitable for master-worker programming models. We have implemented and tested the proposed method in a grid environment using a grid RPC (remote procedure call) system called OmniRPC. The performance of the method on PC clusters that were used over a wide-area network was evaluated.