The 2013 International Joint Conference on Neural Networks (IJCNN)最新文献_第8页

SOMMA: Cortically inspired paradigms for multimodal processing 多模态处理的皮质启发范式

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706959

Mathieu Lefort, Y. Boniface, B. Girau

{"title":"SOMMA: Cortically inspired paradigms for multimodal processing","authors":"Mathieu Lefort, Y. Boniface, B. Girau","doi":"10.1109/IJCNN.2013.6706959","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706959","url":null,"abstract":"SOMMA (Self Organizing Maps for Multimodal Association) consists on cortically inspired paradigms for multimodal data processing. SOMMA defines generic cortical maps - one for each modality - composed of 3-layers cortical columns. Each column learns a discrimination to a stimulus of the input flow with the BCMu learning rule [26]. These discriminations are self-organized in each map thanks to the coupling with neural fields used as a neighborhood function [25]. Learning and computation in each map is influenced by other modalities thanks to bidirectional topographic connections between all maps. This multimodal influence drives a joint self-organization of maps and multimodal perceptions of stimuli. This work takes place after the design of a self-organizing map [25] and of a modulation mechanism for influencing its self-organization [26] oriented towards a multimodal purpose. In this paper, we introduce a way to connect these self-organizing maps to obtain a multimap multimodal processing, completing our previous work. We also give an overview of the architectural and functional properties of the resulting paradigm SOMMA.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124787771","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

Overcoming the local-minimum problem in training multilayer perceptrons by gradual deconvexification 用逐步解麻烦的方法克服多层感知器训练中的局部最小问题

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706796

J. Lo, Yichuan Gui, Yun Peng

引用次数: 8

Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers 数据变异性对基于神经网络的集成分类器的准确性和多样性的影响

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706986

Chien-Yuan Chiu, B. Verma, Michael M. Li

引用次数: 7

Active testing for SVM parameter selection 支持向量机参数选择的主动测试

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706910

P. Miranda, R. Prudêncio

{"title":"Active testing for SVM parameter selection","authors":"P. Miranda, R. Prudêncio","doi":"10.1109/IJCNN.2013.6706910","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706910","url":null,"abstract":"The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. It has been shown that meta-learning can be used to support the selection of SVM parameters. However, it is very dependent on the quality of the dataset and the meta-features used to characterize the dataset. As alternative for this problem, a recent technique called Active Testing characterized a dataset based on the pairwise performance differences between possible solutions. This approach selects the most useful cross-validation tests. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. In this paper we propose the application of Active Testing for the SVM parameter problem. We test it on the problem of setting the RBF kernel parameters for classification problems and we compare its similarity strategy with based on data characteristics. The results showed the variants of Active Testing that rely on cross-validation tests to estimate dataset similarity provides better solutions than those that rely on data characteristics.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127168336","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

Geometrical facial modeling for emotion recognition 面向情感识别的几何面部建模

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707085

Giampaolo L. Libralon, R. Romero

引用次数: 5

The race to new mathematics of brains and consciousness — A tribute to John G. Taylor 大脑和意识的新数学竞赛——致敬约翰·g·泰勒

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706712

R. Kozma

引用次数: 1

Collaborative multi-view clustering 协同多视图聚类

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707037

Mohamad Ghassany, Nistor Grozavu, Younès Bennani

引用次数: 23

Real-time discrete neural identifier for a linear induction motor using a dSPACE DS1104 board 使用dSPACE DS1104板的线性感应电动机的实时离散神经辨识器

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707109

Jorge D. Rios, A. Alanis, J. Rivera, M. Hernández-González

引用次数: 7

About analysis and robust classification of searchlight fMRI-data using machine learning classifiers 基于机器学习分类器的探照灯fmri数据分析与鲁棒分类研究

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706990

M. Lange, M. Kaden, T. Villmann

{"title":"About analysis and robust classification of searchlight fMRI-data using machine learning classifiers","authors":"M. Lange, M. Kaden, T. Villmann","doi":"10.1109/IJCNN.2013.6706990","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706990","url":null,"abstract":"In the present paper we investigate the analysis of functional magnetic resonance image (fMRI) data based on voxel response analysis. All voxels in local spatial area (volume) of a considered voxel form its so-called searchlight. The searchlight for a presented task is taken as a complex pattern. Task dependent discriminant analysis of voxel is then performed by assessment of the discrimination behavior of the respective searchlight pattern for a given task. Classification analysis of these patterns is usually done using linear support vector machines (linSVMs) as a machine learning approach or another statistical classifier like linear discriminant classifier. The test classification accuracy determining the task sensitivity is interpreted as the discrimination ability of the related voxel. However, frequently, the number of voxels contributing to a searchlight is much larger than the number of available pattern samples in classification learning, i.e. the dimensionality of patterns is higher than the number of samples. Therefore, the respective underlying mathematical classification problem has not an unique solution such that a certain solution obtained by the machine learning classifier contains arbitrary (random) components. For this situation, the generalization ability of the classifier may drop down. We propose in this paper another data processing approach to reduce this problem. In particular, we reformulate the classification problem within the searchlight. Doing so, we avoid the dimensionality problem: We obtain a mathematically well-defined classification problem, such that generalization ability of a trained classifier is kept high. Hence, a better stability of the task discrimination is obtained. Additionally, we propose the utilization of generalized learning vector quantizers as an alternative machine learning classifier system compared to SVMs, to improve further the stability of the classifier model due to decreased model complexity.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129214407","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

Adaptive control of discrete-time nonlinear systems by recurrent neural networks in a Quasi Sliding mode regime 基于递归神经网络的离散非线性系统准滑模自适应控制

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706995

I. Salgado, O. C. Nieto, I. Chairez, C. Yáñez-Márquez

引用次数: 0