.png){kind=icon}
返回
An incremental piecewise linear classifier based on polyhedral conic separation
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-014-5449-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a piecewise linear classifier based on polyhedral conic separation is developed. This classifier builds nonlinear boundaries between classes using polyhedral conic functions. Since the number of polyhedral conic functions separating classes is not known a priori, an incremental approach is proposed to build separating functions. These functions are found by minimizing an error function which is nonsmooth and nonconvex. A special procedure is proposed to generate starting points to minimize the error function and this procedure is based on the incremental approach. The discrete gradient method, which is a derivative-free method for nonsmooth optimization, is applied to minimize the error function starting from those points. The proposed classifier is applied to solve classification problems on 12 publicly available data sets and compared with some mainstream and piecewise linear classifiers.
Keyword:
Classification
Polyhedral conic separation
Nonsmooth nonconvex optimization
Discrete gradient method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
引用论文
Comparative analysis of the structural organization of two closely related vitellogenin genes in X. laevis.png)
Cell
CellIF0
PREAI
分享收藏Physiological and behavioral responses to corticotropin-releasing factor administration: is CRF a mediator of anxiety or stress responses?促肾上腺皮质激素释放因子的生理和行为反应: CRF是焦虑或应激反应的中介物吗？
PREAI
分享收藏分享收藏
查看更多内容