Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing最新文献_第2页

Feature-locked loop and its application to image databases 特征锁定循环及其在图像数据库中的应用

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514916

A. Sherstinsky, Rosalind W. Picard

引用次数: 0

A new learning scheme for the recognition of dynamical handwritten characters 一种新的动态手写字符识别学习方案

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514911

F. Andrianasy, M. Milgram

引用次数: 3

Recursive nonlinear identification using multiple model algorithm 多模型递归非线性辨识算法

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514891

V. Kadirkamanathan

引用次数: 8

Learning a distribution-based face model for human face detection 学习基于分布的人脸模型用于人脸检测

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514914

K. Sung, S. Poggio

引用次数: 10

A numerical approach for estimating higher order spectra using neural network autoregressive model 用神经网络自回归模型估计高阶谱的数值方法

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514888

N. Toda, S. Usui

{"title":"A numerical approach for estimating higher order spectra using neural network autoregressive model","authors":"N. Toda, S. Usui","doi":"10.1109/NNSP.1995.514888","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514888","url":null,"abstract":"A method for parametric estimation of higher order spectra of time series using a nonlinear autoregressive model based on multi-layered neural networks (NNAR model) is presented. In real world problems there exist signals that can not be described sufficiently by linear time series models such as AR or ARMA models. In order to characterize such signals, several nonlinear time series models have been investigated in recent years. However, in contrast with the case of linear models, there are a few parametric approaches that estimate the higher order statistical characteristics of observed time series using such nonlinear time series models. It is very difficult to derive analytically explicit formulations of higher order spectra from the expressions of such nonlinear time series models. In this study, employing numerical techniques, the authors construct a parametric estimator of higher order spectra. It consists of the following steps: 1. training an NNAR model on the given time series, 2. iteration of numerical integrals for solving the joint probability density function, 3. calculation of higher order cumulant functions by renewal equations based on the joint probability density function solved in 2., and 4. multidimensional discrete Fourier transforms of higher order cumulant functions calculated in 3. The authors also show that any NNAR model with finite valued weights satisfies a sufficient condition of convergence.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127529290","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: 2

Semiautomated extraction of decision relevant features from a raw data based artificial neural network demonstrated by the problem of saccade detection in EOG recordings of smooth pursuit eye movements 基于人工神经网络的决策相关特征的半自动化提取，以眼动追踪记录中的扫视检测问题为例

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514921

P.K. Tigges, N. Kathmann, R. R. Engel

{"title":"Semiautomated extraction of decision relevant features from a raw data based artificial neural network demonstrated by the problem of saccade detection in EOG recordings of smooth pursuit eye movements","authors":"P.K. Tigges, N. Kathmann, R. R. Engel","doi":"10.1109/NNSP.1995.514921","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514921","url":null,"abstract":"Visual identification of saccades in electrooculographic (EOG) recordings of smooth pursuit eye movements (SPEM) is a very time consuming process for the individual experts. Algorithmic approaches to overcome this problem automatically produce high rates of false positive errors. Artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low. An automated decision process based on modified raw data inputs showed successful proceeding of a backpropagation ANN with an overall performance of 87% correct classifications with previously unknown data. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a sparse and efficient data coding, based on a combination of expert knowledge and the internal representation structures of the ANN. Data coding obtained by this semiautomated procedure yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% and reached an overall correct classification of 92% with unknown data. The proposed method of extracting internal ANN knowledge is not restricted to EOG recordings, and could be used in various fields of signal analysis.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125205259","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: 0

A multiple scale neural system for boundary and surface representation of SAR data SAR数据边界和表面表示的多尺度神经系统

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514905

S. Grossberg, E. Mingolla, J. Williamson

{"title":"A multiple scale neural system for boundary and surface representation of SAR data","authors":"S. Grossberg, E. Mingolla, J. Williamson","doi":"10.1109/NNSP.1995.514905","DOIUrl":"https://doi.org/10.1109/NNSP.1995.514905","url":null,"abstract":"A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar (SAR) sensor. SAR sensors can produce range imagery of high spatial resolution under difficult weather conditions but the data presents some interpretation difficulties. These include the large dynamic range of the sensor signal, which requires some type of nonlinear compression. Another problem is image speckle, which is generated by coherent processing of radar signals and has characteristics of random multiplicative noise. Our approach uses the form-sensitive operations of a neural network model in order to detect and enhance structure based on information over large, variably sized and variably shaped regions of the image. In particular, the multiscale implementation of the neural model reported here is capable of exploiting and combining information from several nested neighborhoods of a given image location to determine the final intensity value to be displayed for that pixel. By \"neighborhood\" is here meant a region whose form varies as a function of nearby image data, not some fixed (weighted) radial function for all pixel locations.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132714303","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: 1

Neural networks for function approximation 函数逼近的神经网络

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514875

H. Mhaskar, L. Khachikyan

引用次数: 13

Active learning the weights of a RBF network 主动学习RBF网络的权值

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514877

K. Sung, P. Niyogi

引用次数: 5

Optimum lag and subset selection for a radial basis function equaliser 径向基函数均衡器的最优滞后和子集选择

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing Pub Date : 1995-08-31 DOI: 10.1109/NNSP.1995.514934

E. Chng, B. Mulgrew, Sheng Chen, Garth A. Gibson

引用次数: 8