{"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}
{"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}
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}
{"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}
{"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}
{"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}
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}
{"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}
{"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}
{"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}