Growing construction of conlitron and multiconlitron

Li, Yujian; Leng, Qiangkui; Yaozong, Fu; Li, Houjun

doi:10.1016/j.knosys.2014.03.024

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…It has been found that building multiple local linear models to approximate a nonlinear separation boundary can adjust and reduce the model flexibility so as to prevent the overfitting. Consequently, it has become a branch of SVM researches . Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Consequently, it has become a branch of SVM a Correspondence to: Jinglu Hu and Benhui Chen. Email: jinglu@waseda.jp; bhchen_dali@163.com researches [5][6][7][8][9]. Zhang et al [7] proposed a method where K nearest neighbors of a sample are used to construct a local linear classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Then, different local linear classifiers were implemented according to different testing samples. Since multiple local linear classifiers have the advantage that they are locally robust to noise for some noisy nonlinear classification problems, this type of methods may perform better than general nonlinear kernel SVMs [9]. In Refs [10,11], we applied an SVM formulation to globally optimize a nonlinear classifier consisting of multiple local linear classifiers interpolated with a basis function.…”

Section: Introductionmentioning

confidence: 99%

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A geometry‐based two‐step method for nonlinear classification using quasi‐linear support vector machine

Zhou

Chen

et al. 2017

IEEJ Transactions Elec Engng

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This paper proposes a two‐step method to construct a nonlinear classifier consisting of multiple local linear classifiers interpolated with a basis function. In the first step, a geometry‐based approach is first introduced to detect local linear partitions and build local linear classifiers. A coarse nonlinear classifier can then be constructed by interpolating the local linear classifiers. In the second step, a support vector machine (SVM) formulation is used to further implicitly optimize the linear parameters of the nonlinear classifier. In this way, the nonlinear classifier is constructed in exactly the same way as a standard SVM, using a special data‐dependent quasi‐linear kernel composed of the information of the local linear partitions. Numerical experiments on several real‐world datasets demonstrate the effectiveness of the proposed classifier and show that, in cases where traditional nonlinear SVMs run into overfitting problems, the proposed classifier is effective in improving the classification performance. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%