2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第9页

Transfer learning for image classification with incomplete multiple sources 不完全多源图像分类的迁移学习

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727470

Zhengming Ding, Ming Shao, Y. Fu

{"title":"Transfer learning for image classification with incomplete multiple sources","authors":"Zhengming Ding, Ming Shao, Y. Fu","doi":"10.1109/IJCNN.2016.7727470","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727470","url":null,"abstract":"Transfer learning plays a powerful role in mitigating the discrepancy between test data (target) and auxiliary data (source). There is often the case that multiple sources are available in transfer learning. However, naively combining multiple sources does not lead to valid results, since they will introduce negative transfer as well. Furthermore, each single source from multiple sources may not cover all the labels of the target data. In this paper, we consider the problem that how to better utilize multiple incomplete sources for effective knowledge transfer. To this end, we propose a Bi-directional Low-Rank Transfer learning framework (BLRT). First, we adapt the conventional low-rank transfer learning to multiple sources knowledge transfer scenario. Second, an iterative structure learning is proposed to better use prior knowledge for transfer learning coefficients matrix. Third, a cross-source regularizer is added to couple the same labels from multiple incomplete sources, so that they could jointly compensate missing data from other sources. Experimental results on three groups of databases including face and object images have demonstrated that our method can successfully inherit knowledge from incomplete multiple sources and adapt to the target data successfully.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130468134","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: 14

Spectral analysis and artificial neural network based classification of three mental states for brain machine interface applications 基于频谱分析和人工神经网络的三种心理状态分类在脑机接口中的应用

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727456

Trongmun Jiralerspong, Sato Fumiya, Chao Liu, J. Ishikawa

{"title":"Spectral analysis and artificial neural network based classification of three mental states for brain machine interface applications","authors":"Trongmun Jiralerspong, Sato Fumiya, Chao Liu, J. Ishikawa","doi":"10.1109/IJCNN.2016.7727456","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727456","url":null,"abstract":"Brain machine interface (BMI) is an emerging technology that aims to assist people with disabilities as well as the aged by allowing their users to intuitively control external devices by intent alone. This paper presents a signal processing technique for a low cost brain machine interface (BMI) that uses spectral analysis and artificial neural network (ANN) to classify three mental states from electroencephalographic (EEG) signals. In this study, a BMI system has been prototyped to classify the intention of moving an object up or down and at rest state. EEG signals are recorded using a consumer grade EEG acquisition device. The device is equipped with 14 electrodes but only 8 electrodes are used in this study. To evaluate the system performance, online classification experiments for three subjects are conducted. True positive and false positive rates are used as an evaluation index. Experiment results show that despite the high difficulty of the mental tasks, the proposed method is capable of achieving an overall true positive rate of up to 67% with 15 minutes of training time by a first time BMI user. Furthermore, offline analysis is carried out using the same EEG data to explore ways of using spectral analysis and ANN to reduce erroneous classifications. Analysis results show that by setting the classification threshold value higher, the false positive rate can be reduced. Another finding suggests that in contrast with the study results by other research teams, the use of multiple ANNs to classify three mental states do not improve the accuracy. Lastly, a hamming window size of 64 samples is found to be optimal for achieving real-time control when performing spectral analysis.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125391317","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

Deep Adaptive Resonance Theory for learning biologically inspired episodic memory 学习生物启发情景记忆的深度自适应共振理论

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727883

Gyeong-Moon Park, Jong-Hwan Kim

{"title":"Deep Adaptive Resonance Theory for learning biologically inspired episodic memory","authors":"Gyeong-Moon Park, Jong-Hwan Kim","doi":"10.1109/IJCNN.2016.7727883","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727883","url":null,"abstract":"Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic memory. Deep ART has an attribute field in the bottom layer, which is newly designed to get semantic information of inputs. After encoding all inputs with their features, events are categorized in the event field using specified inputs. Since an episode is made of a temporal sequence of events, Deep ART makes event sequences with proposed sequence encoding and decoding processes. They can encode any temporal sequence of events, even if there are duplicated events in the episode. Moreover, based on the result of the analysis of retrieval error, Deep ART does not use the complement coding for partial inputs to enhance the accuracy of episode retrieval from partial cues. The simulation results demonstrate the effectiveness of Deep ART as the long term memory.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125409734","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

Node label matching improves classification performance in Deep Belief Networks 节点标签匹配提高了深度信念网络的分类性能

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727395

Allan Campbell, V. Ciesielski, A. K. Qin

引用次数: 0

Co-clustering enterprise social networks 协同集群企业社交网络

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727187

Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, Chengqi Zhang

{"title":"Co-clustering enterprise social networks","authors":"Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, Chengqi Zhang","doi":"10.1109/IJCNN.2016.7727187","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727187","url":null,"abstract":"An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126636064","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: 10

Heuristic dynamic programming for mobile robot path planning based on Dyna approach 基于Dyna方法的启发式动态规划移动机器人路径规划

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727679

Seaar Al Dabooni, D. Wunsch

引用次数: 15

Hyperbolic linear units for deep convolutional neural networks 深度卷积神经网络的双曲线性单元

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727220

Jia Li, Hua Xu, Junhui Deng, Xiaomin Sun

引用次数: 4

Fast Entropy Clustering of sparse high dimensional binary data 稀疏高维二值数据的快速熵聚类

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727497

Marek Śmieja, S. Nakoneczny, J. Tabor

引用次数: 1

How complex-valued multilayer perceptron can predict the behavior of deterministic chaos 复值多层感知器如何预测确定性混沌的行为

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727736

Seiya Satoh, R. Nakano

引用次数: 0

Evolving Spiking Neural Networks of artificial creatures using Genetic Algorithm 利用遗传算法进化人工生物的脉冲神经网络

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727228

E. Eskandari, A. Ahmadi, S. Gomar, M. Ahmadi, M. Saif

引用次数: 8