The Experts below are selected from a list of 28035 Experts worldwide ranked by ideXlab platform

J.t. Kwok - One of the best experts on this subject based on the ideXlab platform.

  • Linear Dependency between /spl epsi/ and the input noise in /spl epsi/-support vector regression
    IEEE Transactions on Neural Networks, 2003
    Co-Authors: J.t. Kwok, I.w. Tsang
    Abstract:

    In using the /spl epsi/-support vector regression (/spl epsi/-SVR) algorithm, one has to decide a suitable value for the insensitivity parameter /spl epsi/. Smola et al. considered its "optimal" choice by studying the statistical efficiency in a location parameter estimation problem. While they successfully predicted a Linear scaling between the optimal /spl epsi/ and the noise in the data, their theoretically optimal value does not have a close match with its experimentally observed counterpart in the case of Gaussian noise. In this paper, we attempt to better explain their experimental results by studying the regression problem itself. Our resultant predicted choice of /spl epsi/ is much closer to the experimentally observed optimal value, while again demonstrating a Linear trend with the input noise.

  • Linear Dependency between e and the input noise in e support vector regression
    International Conference on Artificial Neural Networks, 2001
    Co-Authors: J.t. Kwok
    Abstract:

    In using the e-support vector regression (e-SVR) algorithm, one has to decide on a suitable value of the insensitivity parameter e. Smola et al. [6] determined its “optimal” choice based on maximizing the statistical efficiency of a location parameter estimator. While they successfully predicted a Linear scaling between the optimal e and the noise in the data, the value of the theoretically optimal e does not have a close match with its experimentally observed counterpart. In this paper, we attempt to better explain the experimental results there, by analyzing a toy problem with a closer setting to the e-SVR. Our resultant predicted choice of e is much closer to the experimentally observed value, while still demonstrating a Linear trend with the data noise.

  • Linear Dependency between epsilon and the input noise in epsilon support vector regression
    Lecture Notes in Computer Science, 2001
    Co-Authors: J.t. Kwok
    Abstract:

    In using the Ɛ-support vector regression (Ɛ-SVR) algorithm, one has to decide on a suitable value of the insensitivity parameter Ɛ. Smola et al. [6] determined its "optimal" choice based on maximizing the statistical efficiency of a location parameter estimator. While they successfully predicted a Linear scaling between the optimal Ɛ and the noise in the data, the value of the theoretically optimal Ɛ does not have a close match with its experimentally observed counterpart. In this paper, we attempt to better explain the experimental results there, by analyzing a toy problem with a closer setting to the Ɛ-SVR. Our resultant predicted choice of Ɛ is much closer to the experimentally observed value, while still demonstrating a Linear trend with the data noise.

Masashi Sugiyama - One of the best experts on this subject based on the ideXlab platform.

  • high dimensional feature selection by feature wise kernelized lasso
    Neural Computation, 2014
    Co-Authors: Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
    Abstract:

    The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on Linear Dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonLinear input-output Dependency. We first show that with particular choices of kernel functions, nonredundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures such as the Hilbert-Schmidt independence criterion. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments for classification and regression with thousands of features.

  • High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
    Neural computation, 2013
    Co-Authors: Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
    Abstract:

    The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on Linear Dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-Linear input-output Dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.

Yangquan Chen - One of the best experts on this subject based on the ideXlab platform.

  • robust controllability of interval fractional order Linear time invariant systems
    Signal Processing, 2006
    Co-Authors: Yangquan Chen
    Abstract:

    We consider uncertain fractional-order Linear time invariant (FO-LTI) systems with interval coefficients. Our focus is on the robust controllability issue for interval FO-LTI systems in state-space form. We revisit the controllability problem for the case when there is no interval uncertainty. It turns out that the controllability check for FO-LTI systems amounts to checking the controllability of conventional integer order state space. Based on this fact, we further show that, for interval FO-LTI systems, the key is to check the Linear Dependency of a set of interval vectors. Illustrative examples are presented.

  • robust controllability of interval fractional order Linear time invariant systems
    ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2005
    Co-Authors: Yangquan Chen
    Abstract:

    We consider uncertain fractional-order Linear time invariant (FO-LTI) systems with interval coefficients. Our focus is on the robust controllability issue for interval FO-LTI systems in state-space form. We re-visited the controllability problem for the case when there is no interval uncertainty. It turns out that the stability check for FO-LTI systems amounts to checking the conventional integer order state space using the same state matrix A and the input coupling matrix B. Based on this fact, we further show that, for interval FO-LTI systems, the key is to check the Linear Dependency of a set of interval vectors. Illustrative examples are presented.Copyright © 2005 by ASME

Makoto Yamada - One of the best experts on this subject based on the ideXlab platform.

  • high dimensional feature selection by feature wise kernelized lasso
    Neural Computation, 2014
    Co-Authors: Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
    Abstract:

    The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on Linear Dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonLinear input-output Dependency. We first show that with particular choices of kernel functions, nonredundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures such as the Hilbert-Schmidt independence criterion. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments for classification and regression with thousands of features.

  • High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
    Neural computation, 2013
    Co-Authors: Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama
    Abstract:

    The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on Linear Dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-Linear input-output Dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.

Robert M Obrien - One of the best experts on this subject based on the ideXlab platform.

  • mixed models Linear Dependency and identification in age period cohort models
    Statistics in Medicine, 2017
    Co-Authors: Robert M Obrien
    Abstract:

    This paper examines the identification problem in age-period-cohort models that use either Linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a Linear Dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd.