scholarly journals Adaptive learning rates for support vector machines working on data with low intrinsic dimension

2021 ◽  
Vol 49 (6) ◽  
Author(s):  
Thomas Hamm ◽  
Ingo Steinwart
Keyword(s):  
Support Vector Machines ◽  
Adaptive Learning ◽  
Support Vector ◽  
Intrinsic Dimension ◽  
Learning Rates ◽  
Vector Machines

LEARNING RATES FOR DENSITY LEVEL DETECTION

2005 ◽  
Vol 03 (04) ◽  
pp. 357-371 ◽  
Author(s):  
CLINT SCOVEL ◽  
DON HUSH ◽  
INGO STEINWART
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Density Level ◽  
Free Lunch ◽  
Classification Framework ◽  
Learning Rates ◽  
Vector Machines ◽  
No Free Lunch Theorem ◽  
No Free Lunch

In this paper, we address learning rates for the density level detection (DLD) problem. We begin by proving a "No Free Lunch Theorem" showing that rates cannot be obtained in general. Then, we apply a recently established classification framework to obtain rates for DLD support vector machines under mild assumptions on the density.

A Note on Support Vector Machines with Polynomial Kernels

2016 ◽  
Vol 28 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Hongzhi Tong
Keyword(s):  
Support Vector Machines ◽  
Marginal Distribution ◽  
Theoretical Foundation ◽  
Approximation Error ◽  
Support Vector ◽  
Gaussian Kernels ◽  
Learning Rates ◽  
Vector Machines ◽  
Sample Error ◽  
Polynomial Kernels

We present a better theoretical foundation of support vector machines with polynomial kernels. The sample error is estimated under Tsybakov’s noise assumption. In bounding the approximation error, we take advantage of a geometric noise assumption that was introduced to analyze gaussian kernels. Compared with the previous literature, the error analysis in this note does not require any regularity of the marginal distribution or smoothness of Bayes’ rule. We thus establish the learning rates for polynomial kernels for a wide class of distributions.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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