Proceedings of the International Joint Conference on Neural Networks, 2003.最新文献

筛选
英文 中文
Neural networks applied to classification of data based on Mahalanobis metrics 神经网络应用于基于马氏度量的数据分类
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1224062
A. De Medeiros Martins, A. Neto, J.D. de Melo
{"title":"Neural networks applied to classification of data based on Mahalanobis metrics","authors":"A. De Medeiros Martins, A. Neto, J.D. de Melo","doi":"10.1109/IJCNN.2003.1224062","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1224062","url":null,"abstract":"This work presents a new algorithm for automatic classification of data that make use of a competitive neural network to aid the classification process. The algorithm basically answer two questions: Given a table where each row is a point of dimension D, in how many classes or clusters these data are disposed in? and given a point out of this set, to witch of this classes or clusters the point belongs to? The number of classes is automatically founded by the algorithm, that cluster according with a similarity measure among points that belong to the classes. The similarity measure used was the Mahalanobis distance, instead of the common Euclidian distance. That measure makes possible the incorporation of the spatial statistics of the data.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887950","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
Effects of temporal frequency on speed discrimination and perceived speed 时间频率对速度判别和感知速度的影响
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223336
H. Shen, Y. Shimodaira, G. Ohashi
{"title":"Effects of temporal frequency on speed discrimination and perceived speed","authors":"H. Shen, Y. Shimodaira, G. Ohashi","doi":"10.1109/IJCNN.2003.1223336","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223336","url":null,"abstract":"We examined the effects of temporal frequency of moving stimuli on speed discrimination and perceived speed in human vision, using psychophysical methods. A speed-comparing paradigm displaying two drifting sinusoidal luminance gratings simultaneously was adopted in our experiments. In experimental 1, human vision was found to be most sensitive to the speeds of moving gratings with temporal frequency of about 12 Hz, when speed was above 3.4 deg/sec. It suggests that a mechanism tuned to 12 Hz was involved in speed perception. In experiment 2, stimuli of subjectively equivalent speed (SSES) corresponding to various standard stimuli were determined in the frequency domain. The results showed that the distribution of SSES located along a straight line representing the physical speed of the standard stimuli at low standard speed in the frequency domain, which is considered as depart from the speed-tuning property. However, the distributions began to depart from the speed-tuning property and to approach a temporal-frequency-tuning property at higher speeds above 17 deg/sec. Our conclusion is that perceived speed and speed discrimination have speed-tuning property with normal speeds, but have a non-speed-tuning property with high speeds above 17 deg/sec; a mechanism tuned to 12 Hz is used by speed-tuned mechanisms and used directly by speed perception concurrently.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129777458","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: 5
Evaluation of neural and entropy-constrained routing of communication networks 通信网络的神经和熵约束路由评价
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223998
N. Karayiannis, S.M. Nagabhushan Kaliyur, H. Malki
{"title":"Evaluation of neural and entropy-constrained routing of communication networks","authors":"N. Karayiannis, S.M. Nagabhushan Kaliyur, H. Malki","doi":"10.1109/IJCNN.2003.1223998","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223998","url":null,"abstract":"This paper presents the results of a study that compared the performance of an entropy-constrained routing algorithm with that of routing methods based on neural optimization. The entropy-constrained algorithm was developed to allow multiple nodes of a communication network to compete for each position of a route and it is implemented as a deterministic annealing process. Routing is also performed by the routron and by a routing method relying on the Hopfield-Tank approach to optimization. This experimental study reveals the superiority of the entropy-constrained routing algorithm, which produces consistently the best routes in a small fraction of the time required for route discovery by the neural optimization methods.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129909805","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
An adaptive detection of anomalies in user's behavior 对用户行为异常的自适应检测
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223947
A. Sokolov
{"title":"An adaptive detection of anomalies in user's behavior","authors":"A. Sokolov","doi":"10.1109/IJCNN.2003.1223947","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223947","url":null,"abstract":"We propose an adaptive approach to modeling user's behavior in computer anomaly detection systems. As a base model, Markov chains with variable memory length are used. An adaptive version of the algorithm constructing a model that takes into account changes in user's behavior is introduced. Experimental testing of the proposed approach is also provided.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129540809","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
A neurobiological theory of meaning in perception 关于感知意义的神经生物学理论
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223896
Walter J. Freeman
{"title":"A neurobiological theory of meaning in perception","authors":"Walter J. Freeman","doi":"10.1109/IJCNN.2003.1223896","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223896","url":null,"abstract":"The currency of brains is primarily meaning and only secondarily information. Brain select and pre-process the information carried by sensory stimuli before they construct meaning, and they post-process cognitive meaning into informative commands that control appropriate action and express meaning. A grasp of the intervening process of perception by which meaning is created from information requires observation of neural activity in brains during meaningful behavior of humans and other animals. Measurement is followed by detailed analysis and modeling of the neural activity in order to deduce brain dynamics from brain operations. Unlike computer operation, brain function is hierarchical with many levels. Neurobiologists infer sensory and motor information by recording pulses of microscopic axons of neurons. They infer meaning by recording the local mean fields that are generated by the dendrites of neurons forming interactive mesoscopic and macroscopic populations. Neural populations express meaning in spatial pattern of amplitude modulation of electroencephalographic (EEG) waveforms in the higher frequency ranges of cortical oscillations. Improved theory is needed to understand how brains create meaning. The aim of the theory is to apply population neurodynamics to device new ways to treat clinical brain disorders, and to devise new machines with capacities for autonomy and intelligence that might approach those of simpler free-living animals.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129676220","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: 104
A cascade form blind source separation connecting source separation and linearization for nonlinear mixtures 一种连接非线性混合源分离和线性化的级联形式盲源分离
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223690
K. Nakayama, A. Hirano, Takayuki Nishiwaki
{"title":"A cascade form blind source separation connecting source separation and linearization for nonlinear mixtures","authors":"K. Nakayama, A. Hirano, Takayuki Nishiwaki","doi":"10.1109/IJCNN.2003.1223690","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223690","url":null,"abstract":"A network structure and its learning algorithm have been proposed for blind source separation applied to nonlinear mixtures. The network has a cascade form consists of a source separation block and a linearization block in this order. The conventional learning algorithm is employed for the separation block. A new learning algorithm is proposed for the linearization block assuming 2nd-order nonlinearity. After, source separation, the outputs include the nonlinear components for the same signal source. This nonlinearity is suppressed through the linearization block. Parameters in this block are iteratively adjusted based on a process of solving a 2nd-order equation of a single variable. Simulation results, using 2-channel speech signals and an instantaneous nonlinear mixing process, show good separation performance.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127231907","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
Parallel structured independent component analysis for SIMO-model-based blind separation and deconvolution of convolutive speech mixture 基于simo模型的卷积混合语音盲分离与反卷积并行结构独立分量分析
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223456
H. Saruwatari, H. Yamajo, T. Takatani, T. Nishikawa, K. Shikano
{"title":"Parallel structured independent component analysis for SIMO-model-based blind separation and deconvolution of convolutive speech mixture","authors":"H. Saruwatari, H. Yamajo, T. Takatani, T. Nishikawa, K. Shikano","doi":"10.1109/IJCNN.2003.1223456","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223456","url":null,"abstract":"We propose a two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of temporally correlated signals, in which a new single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under fidelity control of the entire separation system. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. After the separation by SIMO-ICA, a simple blind deconvolution technique based on multichannel inverse filtering for the SIMO model can be applied even when the mixing system is the nonminimum phase system and each source signal is temporally correlated. The experimental results obtained under the reverberant condition reveal that the sound quality of the separated signals in the proposed method is superior to that in the conventional ICA-based BSD.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129950645","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
Invariant feature representation by sparse vectors using adaptive subspace self-organizing map 基于自适应子空间自组织映射的稀疏向量不变特征表示
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223925
T. Zheng
{"title":"Invariant feature representation by sparse vectors using adaptive subspace self-organizing map","authors":"T. Zheng","doi":"10.1109/IJCNN.2003.1223925","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223925","url":null,"abstract":"Extraction of invariant features is a crucial process in pattern recognition. In this paper, a universal framework for encoding invariant features with sparse vectors is described. Using Kohonen's adaptive subspace self-organizing map (ASSOM) [T. Kohonen, 2001], external inputs are encoded in a sparse vector form. These sparse vectors can be used to differentiate the input patterns as well as to represent the invariant features of a pattern category. Experiments using human speech data as an example are described. More importantly, the algorithm can be immediately applied to other forms of input data.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006864","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
Phoneme transcription based on Sampa for Norwegian 基于Sampa的挪威语音素转录
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1223450
T. Kristensen, B. Treeck, R. Falck-Olsen
{"title":"Phoneme transcription based on Sampa for Norwegian","authors":"T. Kristensen, B. Treeck, R. Falck-Olsen","doi":"10.1109/IJCNN.2003.1223450","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1223450","url":null,"abstract":"This paper describes a backpropagation neural network that learns to convert Norwegian text to phonemes. The corpus consists of about 50000 Norwegian words and their transcriptions. The corpus is developed by the Norwegian Telecom research centre. The transcription scheme used is Sampa for Norwegian. The performance of the system has been tested on about 10000 unknown Norwegian words. All the experiments have been done on a PC.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129073218","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
Knowledge-oriented clustering for decision support 面向知识的聚类决策支持
Proceedings of the International Joint Conference on Neural Networks, 2003. Pub Date : 2003-07-20 DOI: 10.1109/IJCNN.2003.1224093
C. Bean, C. Kambhampati
{"title":"Knowledge-oriented clustering for decision support","authors":"C. Bean, C. Kambhampati","doi":"10.1109/IJCNN.2003.1224093","DOIUrl":"https://doi.org/10.1109/IJCNN.2003.1224093","url":null,"abstract":"Cluster analysis is traditionally an unsupervised data. reduction technique. However, by unifying ideas from both cluster analysis and rough set theory, the inherent structure of a data set can be expressed as a rule set which, in turn, can be modified in a supervised manner to obtain a decision rule set with minimal ambiguity. This process of encasing knowledge extraction in an algorithmic framework results in an optimal process for decision support.","PeriodicalId":310128,"journal":{"name":"Proceedings of the International Joint Conference on Neural Networks, 2003.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129132676","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
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
小红书