The 2013 International Joint Conference on Neural Networks (IJCNN)最新文献_第10页

Aircraft sensor estimation for fault tolerant flight control system using fully connected cascade neural network 基于全连接级联神经网络的容错飞控系统传感器估计

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706763

Saed Hussain, M. Mokhtar, J. Howe

引用次数: 8

Constructing mobile-oriented catalog in m-commerce using LDA-based self-adaptive genetic algorithm 基于lda的自适应遗传算法构建移动商务中面向移动的目录

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707112

Hung-Min Hsu, R. Chang, Jan-Ming Ho

{"title":"Constructing mobile-oriented catalog in m-commerce using LDA-based self-adaptive genetic algorithm","authors":"Hung-Min Hsu, R. Chang, Jan-Ming Ho","doi":"10.1109/IJCNN.2013.6707112","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6707112","url":null,"abstract":"The purpose of this paper is to develop a method to recommend products to customer via mobile devices. Collaborative recommendation is known as an effective way to recommend products. In this paper, we use the concept of collaborative recommendation to develop Mobile-Oriented Catalog (MOC). The proposed method is made from aggregating similar purchasing records to optimize combination of goods on mobile devices. This paper illustrates how to design attractive and collaborative catalog to recommend items by using Latent Dirichlet Allocation (LDA) based self-adaptive genetic algorithm (LDA-SAGA). LDA-SAGA is consisted of topic modeling concept and self-adaptive genetic algorithm. We use LDA as our topic modeling algorithm to construct MOC as a result that it is the simplest topic model. Our experimental evaluation on synthetic and real data shows that using preference as topic concept is effective. LDA-SAGA is especially outstanding with large number of customers and products. Finally, we compare the MOC which is used on mobile application (APP) of Amazon with the one used on Taobao and discuss the characteristics of their design. Different design of user interface on APP can lead to different scope of fitness value which is capable of explaining different market strategies of Taobao and Amazon.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"77 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":"122465197","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

Neuromorphic adaptations of restricted Boltzmann machines and deep belief networks 受限玻尔兹曼机与深度信念网络的神经形态适应

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707067

B. Pedroni, Srinjoy Das, E. Neftci, K. Kreutz-Delgado, G. Cauwenberghs

引用次数: 11

Image sequence recognition with active learning using uncertainty sampling 基于不确定性采样的主动学习图像序列识别

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707060

Masatoshi Minakawa, B. Raytchev, Toru Tamaki, K. Kaneda

引用次数: 7

Minimizing validation error with respect to network size and number of training epochs 最小化与网络大小和训练epoch数量相关的验证误差

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706919

Rohit Rawat, Jignesh K. Patel, M. Manry

引用次数: 13

A spatial selective visual attention pattern recognition method based on joint short SSVEP 基于联合短SSVEP的空间选择性视觉注意模式识别方法

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706872

Songyun Xie, Fangshi Zhu, K. Obermayer, P. Ritter, Linan Wang

{"title":"A spatial selective visual attention pattern recognition method based on joint short SSVEP","authors":"Songyun Xie, Fangshi Zhu, K. Obermayer, P. Ritter, Linan Wang","doi":"10.1109/IJCNN.2013.6706872","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706872","url":null,"abstract":"Spatial selective attention pattern recognition plays a significant role in specific people's (e.g.: pilot's) state monitoring. Steady-State Visual Evoked Potentials (SSVEP) were recorded from the scalp of 6 subjects who were cued to attend to a flickering sequence displayed in one visual field while ignoring a similar one with a different flickering rate in the opposite field. The SSVEP to either flickering stimulus was enhanced when attention was lead to the same direction rather than to the opposite direction. The most significant enlargement is generally located on the posterior scalp contralateral to the visual field of stimulation. This attention-caused amplitude enhancement of SSVEP can be used to measure the attention shifting. In this paper, we developed an algorithm to extract short SSVEP, selectively combine them to form a joint temporal spatial selective attention feature, and use Support Vector Machine (SVM) to classify different attention pattern joint features. By segmenting the long single trial SSVEP (12s) data into short pieces (1s), we are able to largely decrease the training time while still keeping a high recognition accuracy (>93%) for most subjects, which makes it possible to monitor spatial selective attention on time.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"106 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":"131609821","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

Variational learning of finite Beta-Liouville mixture models using component splitting 有限Beta-Liouville混合模型的变分学习

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707025

Wentao Fan, N. Bouguila

引用次数: 7

Detecting and labeling representative nodes for network-based semi-supervised learning 基于网络的半监督学习中代表性节点的检测和标记

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706948

Bilzã Araújo, Liang Zhao

{"title":"Detecting and labeling representative nodes for network-based semi-supervised learning","authors":"Bilzã Araújo, Liang Zhao","doi":"10.1109/IJCNN.2013.6706948","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706948","url":null,"abstract":"Network-based Semi-Supervised Learning (NBSSL) propagates labels in networks constructed from the original vector-based data sets taking advantage of the network topology. However, the NBSSL classification performance often varies according to the representativeness of the labeled data instances. Herein, we address this issue. We adopt heuristic criteria for selecting data items for manual labeling based on complex networks centrality measures. The numerical analysis are performed on Girvan and Newman homogeneous networks and Lancichinetti-Fortunato-Radicchi heterogeneous networks. Counterintuitively, we found that the highly connective nodes (hubs) are usually not representative, in the sense that random samples performs as well as them or even better. Other than expected, nodes with high clustering coefficient are good representatives of the data in homogeneous networks. On the other hand, in heterogeneous networks, nodes with high betweenness are the good representatives. A high clustering coefficient means that the node lies in a much connected motif (clique) and a high betweenness means that the node lies interconnecting modular structures. Moreover, aggregating the complex networks measures through Principal Components Analysis, we observed that the second principal component (Z2) exhibits potentially promising properties. It appears that Z2 is able to extract discriminative characteristics allowing finding good representatives of the data. Our results reveal that the performance of the NBSSL can be significantly improved by finding and labeling representative data instances.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"25 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":"127789034","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

Improvement of a neural-fuzzy motion detection vision model for complex scenario conditions 复杂场景条件下神经模糊运动检测视觉模型的改进

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706734

M. Murguia, Graciela Ramírez Alonso, Sergio Gonzalez-Duarte

引用次数: 12

Clustering iOS executable using self-organizing maps 使用自组织映射集群iOS可执行文件

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706728

Fang Yu, Shin-Ying Huang, Li-ching Chiou, R. Tsaih

{"title":"Clustering iOS executable using self-organizing maps","authors":"Fang Yu, Shin-Ying Huang, Li-ching Chiou, R. Tsaih","doi":"10.1109/IJCNN.2013.6706728","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706728","url":null,"abstract":"We pioneer the study on applying both SOMs and GHSOMs to cluster mobile apps based on their behaviors, showing that the SOM family works well for clustering samples with more than ten thousands of attributes. The behaviors of apps are characterized by system method calls that are embedded in their executable, but may not be perceived by users. In the data preprocessing stage, we propose a novel static binary analysis to resolve and count implicit system method calls of iOS executable. Since an app can make thousands of system method calls, it is needed a large dimension of attributes to model their behaviors faithfully. On collecting 115 apps directly downloaded from Apple app store, the analysis result shows that each app sample is represented with 18000+ kinds of methods as their attributes. Theoretically, such a sample representation with more than ten thousand attributes raises a challenge to traditional clustering mechanisms. However, our experimental result shows that apps that have similar behaviors (due to having been developed from the same company or providing similar services) can be clustered together via both SOMs and GHSOMs.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"2016 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":"133120535","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: 6