Computing Radial Basis Function Support Vector Machine using DNA via Fractional Coding

机译：通过分数编码使用DNA计算径向基函数支持向量机

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This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less regression error than that based on implicit conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;

机译：本文介绍了一种合成分子反应以计算径向基函数（RBF）支持向量机（SVM）内核的新颖方法。该方法基于分数编码，其中变量由两个分子表示。分子计算中的分数编码与电子计算中的随机逻辑实现之间的协同作用是将已知的随机逻辑电路转换为分子计算的关键。尽管受RBF-SVM内核的先前随机逻辑实现的启发，但提出的分子反应需要非显而易见的修改。本文介绍了一种用于中间格式转换的新型显式双极至单极分子转换器。提出了两种设计；一个是基于显式的，另一个是基于先前随机逻辑的隐式转换。当使用5个支持向量时，表明使用显式格式转换实现的DNA RBF-SVM比基于隐式转换的回归误差小几个数量级。 CCS概念•应用计算→分子结构生物学； •硬件→生物相关信息处理；

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来源
《2019 56th ACM/IEEE Design Automation Conference》|2019年|1-6|共6页
会议地点 Las Vegas(US)
作者
Xingyi Liu; Keshab K. Parhi;
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作者单位

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN;

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Kernel; Encoding; Logic gates; Support vector machine classification; Molecular computing; DNA;

机译：内核;编码;逻辑门;支持向量机分类;分子计算; DNA;;

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2. Leaf identification using radial basis function neural networks and SSA based support vector machine [J] . Ali Ahmed, Sherif E. Hussein PLoS One . 2020,第8期

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3. Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks [J] . Yuanyao Li, Yifan Sheng, Bo Chai, Geomatics,Natural Hazards & Risk . 2020,第1期

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4. Computing Radial Basis Function Support Vector Machine using DNA via Fractional Coding [C] . Xingyi Liu, Keshab K. Parhi ACM/IEEE Design Automation Conference . 2019

机译：计算径向基函数支持矢量机通过分数编码使用DNA
5. Support vector machines, radial basis functions neural networks, and fuzzy granulation in pattern recognition. [D] . Ramirez, Lino Martin. 2002

机译：支持向量机，径向基函数神经网络和模式识别中的模糊造粒。
6. Leaf identification using radial basis function neural networks and SSA based support vector machine [O] . Ali Ahmed, Sherif E. Hussein 2020

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7. Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification [O] . Abdul Razaque, Mohamed Ben Haj Frej, Muder Almi’ani, 2021

机译：改进的支持向量机使径向基函数和用于遥感图像分类的线性变型

Computing Radial Basis Function Support Vector Machine using DNA via Fractional Coding

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