A Hybrid Mining Model Based on Neural Network and Kernel Smoothing Technique

机译：基于神经网络和核平滑技术的混合挖掘模型

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Neural networks as data mining tools are becoming increasingly popular in business. In this paper, a hybrid mining model based on neural network and kernel smoothing technique is developed. The kernel smoothing technique is used to preprocess data and help decision-making. Neural network is employed to predict the long trends of stock price. In addition, some trading rules involving trading decision-making are considered. The China Shanghai Composite Index is as case study. The return achieved by the hybrid mining model is four times as large as that achieved by the buy and hold strategy, so the proposed model is promising and certainly warrants further research.

机译：神经网络作为数据挖掘工具在商业中正变得越来越流行。本文提出了一种基于神经网络和核平滑技术的混合挖掘模型。内核平滑技术用于预处理数据和帮助决策。神经网络被用来预测股票价格的长期趋势。另外，考虑了一些涉及交易决策的交易规则。中国上证综指是个案研究。混合采矿模型获得的回报是购买和持有策略所获得的回报的四倍，因此所提出的模型是有前途的，当然值得进一步研究。

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来源
《International Conference on Computational Science(ICCS 2005) pt.3; 20050522-25; Atlanta, GA(US)》|2005年|P.801-805|共5页
会议地点 Atlanta GA(US)
作者
Defu Zhang; Qingshan Jiang; Xin Li;
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作者单位

Department of Computer Science, Xiamen University, 361005, China;

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原文格式 PDF
正文语种 eng
中图分类理论、方法;
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机译：一种基于神经网络和核平滑技术的混合挖掘模型

A Hybrid Mining Model Based on Neural Network and Kernel Smoothing Technique

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