Analyzing the Dynamics of the Simultaneous Feature and Parameter Optimization of an Evolving Spiking Neural Network

被引：0

作者：

Schliebs, Stefan ^{[1
]}

Defoin-Platel, Michael ^{[2
]}

Kasabov, Nikola ^{[1
]}

机构：

[1] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand

[2] Biomath & Bioinformat Rothamsted Res, Harpenden, Herts, England

来源：

2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study investigates the characteristics of the Quantum-inspired Spiking Neural Network (QiSNN) feature selection and classification framework. The self-adapting nature of QiSNN due to the simultaneous optimization of network parameters and feature subsets represents a highly desirable characteristic in the context of machine learning and knowledge discovery. In this paper, the evolution of the parameters and feature subsets is studied in detail. The goal of this analysis is a comprehensive understanding of all parameters involved in QiSNN and some practical guidelines for using the method in future research and applications. We also highlight the role of the employed neural encoding technique along with its impact on the classification abilities of QiSNN.

引用

页数：8

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