Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing最新文献

筛选
英文 中文
A probabilistic DBNN with applications to sensor fusion and object recognition 一种概率DBNN及其在传感器融合和目标识别中的应用
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1995.514907
Shang-Hung Lin, S. Kung, Long-Ji Lin
{"title":"A probabilistic DBNN with applications to sensor fusion and object recognition","authors":"Shang-Hung Lin, S. Kung, Long-Ji Lin","doi":"10.1109/NNSP.1995.514907","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514907","url":null,"abstract":"Given an input vector x, a classifier is supposed to tell which class is most likely to have produced it. Thus most data classifiers are designed to have K output nodes corresponding to K classes, {w/sub i/: i=1,...,K}. When pattern classes are clearly separated, this kind of data classifier usually performs very well. A specific model is the decision-based neural network (DBNN), which is effective in many signal/image classification applications. This is particularly the case when pattern classes are clearly separable. However, for those applications which have complex pattern distribution with two or more classes overlapping in pattern space, the traditional DBNN may not be effective or appropriate. For such applications, it is preferable to adopt a probabilistic classifier. In this paper, we develop a new probabilistic variant of the DBNN, which is meant for better estimate probability density functions corresponding to different pattern classes. For this purpose, new learning rules for probabilistic DBNN are derived. In experiments on face databases, we have observed noticeable improvement in various performance measures such as recognition accuracies and, in particular, false acceptance/rejection rates. Taking advantage of probabilistic output values of the DBNN, we construct a multiple sensor fusion system for object classification. In a sense, it represents an extension of the traditional hierarchical structure of DBNN.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129246325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Speaker verification using phoneme-based neural tree networks and phonetic weighting scoring method 基于音素的神经树网络和语音加权评分方法的说话人验证
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1995.514895
Han-Sheng Liou, R. Mammone
{"title":"Speaker verification using phoneme-based neural tree networks and phonetic weighting scoring method","authors":"Han-Sheng Liou, R. Mammone","doi":"10.1109/NNSP.1995.514895","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514895","url":null,"abstract":"A text-dependent speaker verification system based on neural tree network (NTN) phoneme model and phonetic weighting scoring method is presented. The system uses a set of concatenated NTNs trained on phonemes to model a password. In contrast to the conventional stochastic approaches which model the phonemes by hidden Markov models (HMMs), the new approach utilizes the discriminative training scheme to train a NTN for each phoneme. The phoneme-based NTN is trained to discriminate the phoneme spoken by the speaker with respect to those spoken by other speakers. A weighted scoring method depending on the phoneme's ability for speaker verification is used to improve the performance. The proposed system is evaluated by experiments on the YOHO database. Performance improvements are obtained over conventional techniques.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114821604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Globally-ordered topology-preserving maps achieved with a learning rule performing local weight updates only 全局有序的拓扑保持映射,通过只执行局部权重更新的学习规则实现
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1995.514883
M. V. Van Hulle
{"title":"Globally-ordered topology-preserving maps achieved with a learning rule performing local weight updates only","authors":"M. V. Van Hulle","doi":"10.1109/NNSP.1995.514883","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514883","url":null,"abstract":"A new unsupervised competitive learning rule is introduced for topology-preserving map formation and vector quantization. The rule, called maximum entropy learning rule (MER), achieves a globally-ordered map by performing local weight updates only. Hence, contrary to Kohonen's self-organizing map algorithm and its many variations, no neighborhood function is needed. The rule yields an equiprobable quantization of a d-dimensional input p.d.f. Simulations are performed to show that the dynamical- and convergence properties of MER are essentially different from those of Kohonen's algorithm.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
Simultaneous design of feature extractor and pattern classifier using the minimum classification error training algorithm 利用最小分类误差训练算法同时设计特征提取器和模式分类器
Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1900-01-01 DOI: 10.1109/NNSP.1995.514880
K. Paliwal, M. Bacchiani, Y. Sagisaka
{"title":"Simultaneous design of feature extractor and pattern classifier using the minimum classification error training algorithm","authors":"K. Paliwal, M. Bacchiani, Y. Sagisaka","doi":"10.1109/NNSP.1995.514880","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514880","url":null,"abstract":"Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional discriminative training algorithms. We apply this algorithm for simultaneous design of feature extractor and pattern classifier, and demonstrate some of its properties and advantages.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126355962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信