Relationship between SVMs and FANNs and performance comparison in classification
Leng Qiang-kui
Liu Fu-de
Yan Qi
Abstract：This paper presents the relationship between support vector machines ( SVMs) and feed-forward artificial neural networks ( FANNs) .It first illustrates the similarity in terms of network structure , i.e., their in-put function can be expressed as a linear combination of basis functions .Then, some key differences between SVMs and FANNs are pointed out .One is the difference of optimization goal .FANNs only needs to reach empiri-cal risk minimization , but for SVMs , it aims at minimizing the structural risk .Another is the difference of hidden layer.In SVMs, the nodes of hidden layer are exactly support vectors .While in FANNs, the nodes of hidden layer can be empirically determined in advance .Moreover , the difference of model complexity is also concerned . For FANNs, its complexity is controlled by the number of hidden nodes .But the complexity of SVMs is inde-pendent of data dimensionality .At the end, the comparative experiments on UCI benchmark datasets are provid-ed, for evaluating the performance of SVMs and FANNs in classification .
Keywords：pattern recognitionSVMsFANNsdeep learning
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 254-260 )