{"title":"模式识别系统的多层次反向传播网络","authors":"C.Y. Chen, C. Hwang","doi":"10.1109/ICNN.1994.374724","DOIUrl":null,"url":null,"abstract":"The backpropagation network (BPN) is now widely used in the field of pattern recognition because this artificial neural network can classify complex patterns and perform nontrivial mapping functions. In this paper, we propose a multi-level backpropagation network (MLBPN) model as a classifier for practical pattern recognition systems. The described model reserves the benefits of the BPN and derives the extra benefits of this MLBPN with two fold: (1) the MLBPN can reduce the complexity of BPN, and (2) a speed-up of the recognition process is attained. The experimental results verify these characteristics and show that the MLBPN model is a practical classifier for pattern recognition systems.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A multi-level backpropagation network for pattern recognition systems\",\"authors\":\"C.Y. Chen, C. Hwang\",\"doi\":\"10.1109/ICNN.1994.374724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The backpropagation network (BPN) is now widely used in the field of pattern recognition because this artificial neural network can classify complex patterns and perform nontrivial mapping functions. In this paper, we propose a multi-level backpropagation network (MLBPN) model as a classifier for practical pattern recognition systems. The described model reserves the benefits of the BPN and derives the extra benefits of this MLBPN with two fold: (1) the MLBPN can reduce the complexity of BPN, and (2) a speed-up of the recognition process is attained. The experimental results verify these characteristics and show that the MLBPN model is a practical classifier for pattern recognition systems.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-level backpropagation network for pattern recognition systems
The backpropagation network (BPN) is now widely used in the field of pattern recognition because this artificial neural network can classify complex patterns and perform nontrivial mapping functions. In this paper, we propose a multi-level backpropagation network (MLBPN) model as a classifier for practical pattern recognition systems. The described model reserves the benefits of the BPN and derives the extra benefits of this MLBPN with two fold: (1) the MLBPN can reduce the complexity of BPN, and (2) a speed-up of the recognition process is attained. The experimental results verify these characteristics and show that the MLBPN model is a practical classifier for pattern recognition systems.<>
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