The Support Vector Regression with Adaptive Norms | Zendy

Chunhua Zhang | Zendy; Dewei Li | Zendy; Junyan Tan | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

The Support Vector Regression with Adaptive Norms

Author(s) -

Chunhua Zhang,

Dewei Li,

Junyan Tan

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.341

Subject(s) - computer science , hinge loss , support vector machine , norm (philosophy) , regression , penalty method , regression analysis , mathematical optimization , function (biology) , algorithm , artificial intelligence , machine learning , mathematics , statistics , evolutionary biology , political science , law , biology

This study proposes a new method for regression – lp-norm support vector regression (lp SVR). Some classical SVRs minimize the hinge loss function subject to the l2-norm or l1-norm penalty. These methods are non-adaptive since their penalty forms are fixed and pre-determined for any types of data. Our new model is an adaptive learning procedure with lp-norm (0 < p < 1), where the best p is automatically chosen by data. By adjusting the parameter p, lp SVR can not only select relevant features but also improve the regression accuracy. An iterative algorithm is suggested to solve the lp SVR efficiently. Simulations and real data applications support the effectiveness of the proposed procedure

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research