基于神经网络的考莫吉原形多类分类检测

International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management Pub Date : 2019-09-17 DOI:10.5220/0008366203770382

Noriyuki Okumura, Rei Okumura

{"title":"基于神经网络的考莫吉原形多类分类检测","authors":"Noriyuki Okumura, Rei Okumura","doi":"10.5220/0008366203770382","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a multi-class classification method for Kaomoji using feed forward neural network. Neural network has some units in each layer, but the suitable number of units is not clear. This research investigated the relation between the number of units and the accuracy of multi-class classification method.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network\",\"authors\":\"Noriyuki Okumura, Rei Okumura\",\"doi\":\"10.5220/0008366203770382\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a multi-class classification method for Kaomoji using feed forward neural network. Neural network has some units in each layer, but the suitable number of units is not clear. This research investigated the relation between the number of units and the accuracy of multi-class classification method.\",\"PeriodicalId\":133533,\"journal\":{\"name\":\"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0008366203770382\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0008366203770382","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

本文提出了一种基于前馈神经网络的考莫吉多类分类方法。神经网络的每一层都有一定的单元，但合适的单元个数并不明确。本研究探讨了多类分类方法的单位数与准确率之间的关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Multi Class Classification to Detect Original Form of Kaomoji using Neural Network

In this paper, we propose a multi-class classification method for Kaomoji using feed forward neural network. Neural network has some units in each layer, but the suitable number of units is not clear. This research investigated the relation between the number of units and the accuracy of multi-class classification method.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

自引率

0.00%

发文量