Add to ORKGThis paper is a selective review of the regularization methods scattered in statistics literature. We introduce a general conceptual approach to regularization and fit most existing methods into it. We have tried to focus on the importance of regularization when dealing with today's high-dimensional objects: data and models. A wide range of examples are discussed, including nonparametric regression, boosting, covariance matrix estimation, principal component estimation, subsampling
A new regularization method for regression models is proposed. The criterion to be minimized contain...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment....
Regularization, linear regression, nonparametric regression, boosting, covariance matrix, principal ...
We begin with a few historical remarks about what might be called the regularization class of statis...
The purpose of this chapter is to present a theoretical framework for the problem of learning from e...
A range of regularization approaches have been proposed in the data sciences to overcome overfitting...
Regularization is a standard statistical technique to deal with ill-conditioned parameter estimation...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Using a collection of simulated an real benchmarks, we compare Bayesian and frequentist regularizati...
The prevention sciences often face several situations that can compromise the statistical power and ...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
<p>A) A two-dimensional example illustrate how a two-class classification between the two data sets ...
Researchers and data analysts are sometimes faced with the problem of very small samples, where the ...
This diploma thesis focuses on regularization and variable selection in regres- sion models. Basics ...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment....
Regularization, linear regression, nonparametric regression, boosting, covariance matrix, principal ...
We begin with a few historical remarks about what might be called the regularization class of statis...
The purpose of this chapter is to present a theoretical framework for the problem of learning from e...
A range of regularization approaches have been proposed in the data sciences to overcome overfitting...
Regularization is a standard statistical technique to deal with ill-conditioned parameter estimation...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Using a collection of simulated an real benchmarks, we compare Bayesian and frequentist regularizati...
The prevention sciences often face several situations that can compromise the statistical power and ...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
<p>A) A two-dimensional example illustrate how a two-class classification between the two data sets ...
Researchers and data analysts are sometimes faced with the problem of very small samples, where the ...
This diploma thesis focuses on regularization and variable selection in regres- sion models. Basics ...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
A new regularization method for regression models is proposed. The criterion to be minimized contain...
Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment....