Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing最新文献_第3页

Real-time source separation based on sound localization in a reverberant environment 基于混响环境中声音定位的实时声源分离

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030059

M. Aoki, K. Furuya

引用次数: 3

Fusion of multiple experts in multimodal biometric personal identity verification systems 多模态生物识别个人身份验证系统中多专家的融合

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030012

J. Kittler, K. Messer

引用次数: 37

A new SOLPN-based rate control algorithm for MPEG video coding 一种新的基于solpn的MPEG视频编码速率控制算法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030069

Zhiming Zhang, Seung-Gi Chang, Jeonghoon Park, Yongje Kim

引用次数: 0

Temporal associative memory and function approximation with the self-organizing map 时间联想记忆与自组织映射的功能逼近

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030022

G. Barreto, A. Araujo

引用次数: 6

On learning feedforward neural networks with noise injection into inputs 输入带噪声的前馈神经网络的学习

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030026

A. Seghouane, Y. Moudden, G. Fleury

{"title":"On learning feedforward neural networks with noise injection into inputs","authors":"A. Seghouane, Y. Moudden, G. Fleury","doi":"10.1109/NNSP.2002.1030026","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030026","url":null,"abstract":"Injecting noise to the inputs during the training of feedforward neural networks (FNN) can improve their generalization performance remarkably. Reported works justify this fact arguing that noise injection is equivalent to a smoothing regularization with the input noise variance playing the role of the regularization parameter. The success of this approach depends on the appropriate choice of the input noise variance. However, it is often not known a priori if the degree of smoothness imposed on the FNN mapping is consistent with the unknown function to be approximated. In order to have a better control over this smoothing effect, a cost function putting in balance the smoothed fitting induced by the noise injection and the precision of approximation, is proposed. The second term, which aims at penalizing the undesirable effect of input noise injection or controlling the deviation of the random perturbed cost, was obtained by expressing a certain distance between the original cost function and its random perturbed version. In fact, this term can be derived in general for parametrical. models that satisfy the Lipschitz property. An example is included to illustrate the effectiveness of learning with this proposed cost function when noise injection is used.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024927","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: 7

Multi-layer perceptron mapping on a SIMD architecture 基于SIMD架构的多层感知器映射

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030078

S. Vitabile, A. Gentile, G. B. Dammone, F. Sorbello

引用次数: 18

A multi-sample multi-source model for biometric authentication 一个多样本多源的生物识别认证模型

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030049

N. Poh, Samy Bengio, J. Korczak

引用次数: 59

Detection of unusual human behavior in intelligent house 智能住宅中人类异常行为的检测

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030081

K. Hara, T. Omori, Reiko Ueno

引用次数: 57

Robust classification of subcellular location patterns in fluorescence microscope images 荧光显微镜图像中亚细胞定位模式的鲁棒分类

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030018

R. Murphy, M. Velliste, G. Porreca

{"title":"Robust classification of subcellular location patterns in fluorescence microscope images","authors":"R. Murphy, M. Velliste, G. Porreca","doi":"10.1109/NNSP.2002.1030018","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030018","url":null,"abstract":"The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123388631","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: 36

A fingerprint segmentation method using a recurrent neural network 一种基于递归神经网络的指纹分割方法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI: 10.1109/NNSP.2002.1030046

S. Sato, T. Umezaki

引用次数: 3