Now showing items 1-5 of 5

  • A Bayesian Approach to Empirical Local Linearization For Robotics 

    Ting, Jo-Anne; D'Souza, Aaron; Vijayakumar, Sethu; Schaal, Stefan (2008)
    Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics systems where the dynamics and kinematics are ...
  • The Bayesian Backfitting Relevance Vector Machine 

    D'Souza, Aaron; Vijayakumar, Sethu; Schaal, Stefan (ACM Press, 2004-07)
    Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art Bayesian algorithms which, however, ...
  • Efficient Learning and Feature Selection in High Dimensional Regression 

    Ting, Jo-Anne; D'Souza, Aaron; Vijayakumar, Sethu; Schaal, Stefan (MIT Press, 2010)
    We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a ...
  • Incremental Online Learning in High Dimensions 

    Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan (MIT Press, 2005-12)
    Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core
  • LWPR: A Scalable Method for Incremental Online Learning in High Dimensions 

    Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan (2005-06)
    Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear func- tion approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs ...