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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.最新文献

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Chaotic associative memory and private v-mails 混乱的联想记忆和私人电子邮件
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556312
H. Szu, M. Hsu
{"title":"Chaotic associative memory and private v-mails","authors":"H. Szu, M. Hsu","doi":"10.1109/IJCNN.2005.1556312","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556312","url":null,"abstract":"To support the 3rd generation cellular phone with broad band wireless video emails (Vmails), we develope a chaotic neural network (CNN) associative memory whose tying and initial value were sent by a private and free version of RSA security algorithm (patent expired). Receiver devices with the embedded system chip regenerate the specific CNN image series, so called the spatial-temporal keys (STK), which allow the RSA and the V-mail data to be correctly decrypted. Due to the fading and fatal noise in wireless communication channel, the STK must be robust and fault-tolerant proved by a field theory of associative memory and demonstrated by a collective fixed point of cycles of the whole bifurcated images.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125637210","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
Neural network based detection of fetal heart rate patterns 基于神经网络的胎儿心率模式检测
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556278
P. Warrick, E. Hamilton, M. Macieszczak
{"title":"Neural network based detection of fetal heart rate patterns","authors":"P. Warrick, E. Hamilton, M. Macieszczak","doi":"10.1109/IJCNN.2005.1556278","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556278","url":null,"abstract":"Automated detection of fetal heart rate (FHR) patterns can potentially improve intra-partum care by providing consistent and reliable measures that assist health-care professionals in their assessment of the state of the fetus. We use the combined tools of signal processing and neural networks to detect the FHR patterns of baseline, acceleration and deceleration. Comparison to previous results reported in the literature are provided.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032937","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: 37
A novel image retrieval system based on BP neural network 一种基于BP神经网络的图像检索系统
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556306
Jun-Hua Han, De-shuang Huang, T. Lok, M.R. Lyu
{"title":"A novel image retrieval system based on BP neural network","authors":"Jun-Hua Han, De-shuang Huang, T. Lok, M.R. Lyu","doi":"10.1109/IJCNN.2005.1556306","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556306","url":null,"abstract":"This paper presents a novel BP-based image retrieval (BPBIR) system, which is based on the observation that the images users need are often similar to a set of images with the same conception instead of one query image and the assumption that there is a nonlinear relationship between different features. If users aren't satisfied with the retrieved results, relevance feedback method is used to enhance the performance of the proposed system by changing the weights of the BP neural networks. In addition, we discuss some divisional methods to give rough information on the spatial color composition. Finally, we compare the performance of the proposed system with other systems. Experimental results show the efficacy of the proposed system.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122924917","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
MIMO SVMs for classification and regression using the geometric algebra framework MIMO支持向量机的分类和回归使用几何代数框架
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555971
E. Bayro-Corrochano, Nancy Arana-Daniel
{"title":"MIMO SVMs for classification and regression using the geometric algebra framework","authors":"E. Bayro-Corrochano, Nancy Arana-Daniel","doi":"10.1109/IJCNN.2005.1555971","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555971","url":null,"abstract":"This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real- and complex-valued support vector machines using the Clifford geometric algebra. In this framework we handle the design of kernels involving the Clifford or geometric product for linear and nonlinear classification and regression. The major advantage of our approach is that one requires only one CSVM with one kernel (involving the Clifford product) which can admit multiple multivector inputs and it can carry out multi-class classification and regression. In contrast one would need many real valued SVMs for a multi-class problem which is time consuming.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"152 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131212320","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
Connectivity of anatomical and functional MRI data 解剖和功能MRI数据的连通性
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556105
K. Worsley, A. Charil, J. Lerch, A. Evans
{"title":"Connectivity of anatomical and functional MRI data","authors":"K. Worsley, A. Charil, J. Lerch, A. Evans","doi":"10.1109/IJCNN.2005.1556105","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556105","url":null,"abstract":"We are all familiar with the correlation coefficient between two sets of numbers. Now suppose we replace the numbers by vector-valued images in any number of dimensions. The correlation random field is the 'image' of correlations at all possible pairs of points in the two images. We use random field theory to set a threshold on the correlations so that those above the threshold are statistically significant, corrected for searching over all pairs of points. We apply this idea to resting state networks of fMRI images of brain activity, and networks of connectivity in cortical thickness.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131229861","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: 23
Stochastic feature selection for the discrimination of biomedical spectra 生物医学光谱识别的随机特征选择
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556408
N. Pizzi, M. Alexiuk, W. Pedrycz
{"title":"Stochastic feature selection for the discrimination of biomedical spectra","authors":"N. Pizzi, M. Alexiuk, W. Pedrycz","doi":"10.1109/IJCNN.2005.1556408","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556408","url":null,"abstract":"When dealing with the curse of dimensionality (small sample size with many dimensions), feature subset selection is an important preprocessing strategy. This issue is particularly germane to the discrimination of class-labeled high-dimensional biomedical spectra as is often acquired from magnetic resonance and infrared spectrometers. A technique is presented that stochastically selects feature subsets with varying cardinality for discrimination by probabilistic neural networks. The results are benchmarked against two classifiers using the entire feature set both with and without feature averaging. The new technique had significantly fewer misclassifications than either of the benchmarks.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121783697","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
Learning probability density functions from marginal distributions with applications to Gaussian mixtures 学习概率密度函数从边际分布与应用到高斯混合物
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556015
Qutang Cai, Changshui Zhang, Chunyi Peng
{"title":"Learning probability density functions from marginal distributions with applications to Gaussian mixtures","authors":"Qutang Cai, Changshui Zhang, Chunyi Peng","doi":"10.1109/IJCNN.2005.1556015","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556015","url":null,"abstract":"Probability density function (PDF) estimation is a constantly important topic in the fields related to artificial intelligence and machine learning. This paper is dedicated to considering problems on the estimation of a density function simply from its marginal distributions. The possibility of the learning problem is first investigated and a uniqueness proposition involving a large family of distribution functions is proposed. The learning problem is then reformulated into an optimization task which is studied and applied to Gaussian mixture models (GMM) via the generalized expectation maximization procedure (GEM) and Monte Carlo method. Experimental results show that our approach for GMM, only using partial information of the coordinates of the samples, can obtain satisfactory performance, which in turn verifies the proposed reformulation and proposition.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121251540","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: 3
Feedback linearization using neural networks applied to advanced pharmacodynamic and pharmacogenomic systems 应用于先进药效学和药物基因组学系统的神经网络反馈线性化
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555825
A. Floares
{"title":"Feedback linearization using neural networks applied to advanced pharmacodynamic and pharmacogenomic systems","authors":"A. Floares","doi":"10.1109/IJCNN.2005.1555825","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555825","url":null,"abstract":"Pharmacological modeling is developing from an empirical discipline into a mechanistic science. Also, new and important fields like pharmacogenomics appeared. As a consequence, pharmacology is dealing with high dimensional, nonlinear, control systems. The intent of this paper is to show that all this systems, being based on a limited array of mechanisms and having some structural peculiarities, are good candidate for the application of feedback linearization techniques, using neural networks. Unlike Jacobian linearization, feedback linearization is not only locally valid. The proposed protocol can be applied even without the aid of a mathematical model. A drug dosage regimen, established in this way, will determine the output of the pharmacological system to track very well the therapeutic objective. To the best of author's knowledge, this is the first time when a very large class of complex pharmacological problems are formulated and solved in terms of neural network control.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127588169","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: 11
Introduction of a Hebbian unsupervised learning algorithm to boost the encoding capacity of Hopfield networks 引入一种Hebbian无监督学习算法来提高Hopfield网络的编码能力
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1556109
C. Molter, U. Salihoglu, H. Bersini
{"title":"Introduction of a Hebbian unsupervised learning algorithm to boost the encoding capacity of Hopfield networks","authors":"C. Molter, U. Salihoglu, H. Bersini","doi":"10.1109/IJCNN.2005.1556109","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1556109","url":null,"abstract":"The learning impact, of an iterative supervised Hebbian learning algorithm, on a recurrent neural network's underlying dynamics has been discussed in a previous paper. It was argued that these results are in line with the observations made by Freeman in the olfactory bulb of the rabbit: cycles are used to store information and the chaotic dynamics appears as the background regime composed of those cyclic \"memory bags\". However, to get closer to a biological point of view, this paper introduces an unsupervised version of this Hebbian algorithm. As a direct result, both the storing capacity and the content addressability of the learned networks are greatly enhanced. Furthermore, stunning dynamical results are observed: if the learning process increases the dimension of the potential attractors, however, less chaoticity is found than in a supervised learning process. Moreover, chaos obtained looks more structured, made from brief itinerancy among learned cycles.","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657750","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: 18
Attention as sigma-pi controlled ACh-based feedback 注意力作为sigma-pi控制的基于ach的反馈
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI: 10.1109/IJCNN.2005.1555839
John Taylor, M. Hartley, N. Taylor
{"title":"Attention as sigma-pi controlled ACh-based feedback","authors":"John Taylor, M. Hartley, N. Taylor","doi":"10.1109/IJCNN.2005.1555839","DOIUrl":"https://doi.org/10.1109/IJCNN.2005.1555839","url":null,"abstract":"We analyse experimental data on attention to indicate that any attention feedback control signals to lower order cortical sites will lead to a quadratic sigma-pi form of output in its dependence on the lower-order input and the feedback signal. The manner by which this structure works is shown by a brief simulation. We then discuss how such a structure could arise from the action of diffuse acetylcholine signals from the NBM, especially involving nicotinic receptors. We deduce certain structural regularities which should be expected both at local-and at micro-circuit level, mainly in cortical layer V (the output layer).","PeriodicalId":365690,"journal":{"name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129394","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: 12
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