Fast Generalized Cross-Validation Algorithm for Sparse Model Learning (2007)
Sundararajan, S, Shevade, Shirish, Keerthi, Sathiya S
We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized...
Fast Generalized Cross-Validation Algorithm for Sparse Model Learning (2007)
Sundararajan, S, Shevade, Shirish, Keerthi, Sathiya S
We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized...
Predictive Approaches for Sparse Model Learning (2004)
Shevade, SK, Sundararajan, S, Keerthi, SS
In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing...
Predictive Approaches for Sparse Model Learning (2004)
Shevade, SK, Sundararajan, S, Keerthi, SS
In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing...
Predictive approaches for choosing hyperparameters in Gaussian processes (1999)
S. Sundararajan, S. Sathiya Keerthi
Gaussian Processes are powerful regression models specied by parametrized mean and covariance functions. Standard approaches to estimate these parameters (known by the name Hyperparameters) are...
Predictive approaches for choosing hyperparameters in Gaussian processes (1999)
S. Sundararajan, S. S. Keerthi
Gaussian Processes are powerful regression models specied by parameterized mean and covariance functions. Standard approaches to choose these parameters (known by the name hyperparameters) are...