Efficient Model Selection for Sparse Least-Square SVMs

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Vector Quantization as Sparse Least Square Optimization

Vector quantization aims to form new vectors/matrices with shared values close to the original. It could compress data with acceptable information loss, and could be of great usefulness in areas like Image Processing, Pattern Recognition and Machine Learning. In recent years, the importance of quantization has been soaring as it has been discovered huge potentials in deploying practical neural ...

متن کامل

Model Selection for Multi-class SVMs

In the framework of statistical learning, fitting a model to a given problem is usually done in two steps. First, model selection is performed, to set the values of the hyperparameters. Second, training results in the selection, for this set of values, of a function performing satisfactorily on the problem. Choosing the values of the hyperparameters remains a difficult task, which has only been...

متن کامل

Using an Efficient Penalty Method for Solving Linear Least Square Problem with Nonlinear Constraints

In this paper, we use a penalty method for solving the linear least squares problem with nonlinear constraints. In each iteration of penalty methods for solving the problem, the calculation of projected Hessian matrix is required. Given that the objective function is linear least squares, projected Hessian matrix of the penalty function consists of two parts that the exact amount of a part of i...

متن کامل

Fast orthogonal least squares algorithm for efficient subset model selection

An efficient implementation of the orthogonal least squares algorithm for subset model selection is derived in this correspondence. Computational complexity of the algorithm is examined and the result shows that this new fast orthogonal least squares algorithm significantly reduces computational requirements.

متن کامل

Least Squares After Model Selection in High-dimensional Sparse Models

In this paper we study post-model selection estimators which apply ordinary least squares (ols) to the model selected by first-step penalized estimators, typically lasso. It is well known that lasso can estimate the nonparametric regression function at nearly the oracle rate, and is thus hard to improve upon. We show that ols post lasso estimator performs at least as well as lasso in terms of t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Mathematical Problems in Engineering

سال: 2013

ISSN: 1024-123X,1563-5147

DOI: 10.1155/2013/712437