A deeper Newton descent direction with generalised Hessian matrix for SVMs: an application to face detection
by Abdessamad Amir; Korichi Mokhtar El Amine
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 11, No. 2, 2021

Abstract: By formulating the generalised Newton descent direction according to the parameter resulting from the calculation of the subgradient of the max function, a new version of NSVM (Fung and Mangasarian, 2004) is presented in this paper. This descent direction ensures even more the precision of the solution in a fast time. Associated with a good features extraction technique like Gabor's wavelets, the application of the proposed method in the context of facial detection shows that either the direction is calculated as an optimal solution of a one-dimensional problem or by a heuristic approach, manages to detect faces not detected by advanced methods.

Online publication date: Fri, 23-Apr-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com