scholarly journals Large-margin classification with multiple decision rules

2016 ◽  
Vol 9 (2) ◽  
pp. 89-105
Author(s):  
Patrick K. Kimes ◽  
David Neil Hayes ◽  
J. S. Marron ◽  
Yufeng Liu ◽  
Keyword(s):  
Decision Rules ◽  
Large Margin ◽  
Large Margin Classification ◽  
Margin Classification

Margin Error Bounds for Support Vector Machines on Reproducing Kernel Banach Spaces

2017 ◽  
Vol 29 (11) ◽  
pp. 3078-3093 ◽  
Author(s):  
Liangzhi Chen ◽  
Haizhang Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Banach Spaces ◽  
Reproducing Kernel ◽  
Support Vector ◽  
Large Margin ◽  
Large Margin Classification ◽  
Vector Machines ◽  
Reproducing Kernel Banach Spaces ◽  
Margin Classification

Support vector machines, which maximize the margin from patterns to the separation hyperplane subject to correct classification, have received remarkable success in machine learning. Margin error bounds based on Hilbert spaces have been introduced in the literature to justify the strategy of maximizing the margin in SVM. Recently, there has been much interest in developing Banach space methods for machine learning. Large margin classification in Banach spaces is a focus of such attempts. In this letter we establish a margin error bound for the SVM on reproducing kernel Banach spaces, thus supplying statistical justification for large-margin classification in Banach spaces.

scholarly journals Large-Margin Classification in Infinite Neural Networks

2010 ◽  
Vol 22 (10) ◽  
pp. 2678-2697 ◽  
Author(s):  
Youngmin Cho ◽  
Lawrence K. Saul
Keyword(s):  
Neural Networks ◽  
Support Vector ◽  
Positive Definite Kernels ◽  
Large Margin ◽  
New Family ◽  
Large Margin Classification ◽  
Feature Spaces ◽  
Vector Machines ◽  
Nonlinear Feature ◽  
Margin Classification

We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived kernels mimic the computation in deep networks with multiple hidden layers. We evaluate SVMs with these kernels on problems designed to illustrate the advantages of deep architectures. Compared to previous benchmarks, we find that on some problems, these SVMs yield state-of-the-art results, beating not only other SVMs but also deep belief nets.

An evolutionary algorithm for large margin classification

2021 ◽  
Author(s):  
Renan Motta Goulart ◽  
Carlos Cristiano Hasenclever Borges ◽  
Raul Fonseca Neto
Keyword(s):  
Evolutionary Algorithm ◽  
Large Margin ◽  
Large Margin Classification ◽  
Margin Classification

scholarly journals Multicategory large margin classification methods: Hinge losses vs. coherence functions

2014 ◽  
Vol 215 ◽  
pp. 55-78 ◽  
Author(s):  
Zhihua Zhang ◽  
Cheng Chen ◽  
Guang Dai ◽  
Wu-Jun Li ◽  
Dit-Yan Yeung
Keyword(s):  
Classification Methods ◽  
Large Margin ◽  
Large Margin Classification ◽  
Margin Classification
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