The 2013 International Joint Conference on Neural Networks (IJCNN)最新文献_第7页

Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances 基于自适应马氏距离的自组织图的区间数据聚类

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706852

Chantal Hajjar, H. Hamdan

引用次数: 33

Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures 基于备选鲁棒性测度的鲁棒极点配置神经动力学优化方法

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706834

Xinyi Le, Jun Wang

引用次数: 5

The convergence rate of linearly separable SMO 线性可分SMO的收敛速度

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6707034

J. Lázaro, José R. Dorronsoro

引用次数: 1

Autism spectrum disorder detection using projection based learning meta-cognitive RBF network 基于投影学习元认知RBF网络的自闭症谱系障碍检测

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706777

Vigneshwaran Senthilvel, B. S. Mahanand, S. Sundaram, R. Savitha

{"title":"Autism spectrum disorder detection using projection based learning meta-cognitive RBF network","authors":"Vigneshwaran Senthilvel, B. S. Mahanand, S. Sundaram, R. Savitha","doi":"10.1109/IJCNN.2013.6706777","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706777","url":null,"abstract":"In this paper, we present an approach for the diagnosis of Autism Spectrum Disorder (ASD) from Magnetic Resonance Imaging (MRI) scans with Voxel-Based Morphometry (VBM) detected features using Projection Based Learning (PBL) algorithm for a Meta-cognitive Radial Basis Function Network (McRBFN) classifier. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, the McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. Moreover, as samples with similar information are deleted, over-training is avoided. The PBL algorithm helps to reduce the computational effort used in training. For simulation studies, we have used MR images from the Autism Brain Imaging Data Exchange (ABIDE) data set. The performance of the PBL-McRBFN classifier is evaluated on complete morphometric features set obtained from the VBM analysis. The performance evaluation study clearly indicates the superior performance of PBL-McRBFN classifier over other classification algorithms.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"34 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":"114586738","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: 20

Classificability-regulated self-organizing map using restricted RBF 基于受限RBF的可分类调节自组织映射

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706732

P. Hartono, T. Trappenberg

引用次数: 8

Exogenous control and dynamical reduction of echo state networks 回声状态网络的外生控制与动态缩减

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706898

Patrick Stinson, Keith A. Bush

引用次数: 0

Robust human action recognition via long short-term memory 基于长短期记忆的稳健人类动作识别

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706797

Alexander Grushin, Derek Monner, J. Reggia, A. Mishra

{"title":"Robust human action recognition via long short-term memory","authors":"Alexander Grushin, Derek Monner, J. Reggia, A. Mishra","doi":"10.1109/IJCNN.2013.6706797","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706797","url":null,"abstract":"The long short-term memory (LSTM) neural network utilizes specialized modulation mechanisms to store information for extended periods of time. It is thus potentially well-suited for complex visual processing, where the current video frame must be considered in the context of past frames. Recent studies have indeed shown that LSTM can effectively recognize and classify human actions (e.g., running, hand waving) in video data; however, these results were achieved under somewhat restricted settings. In this effort, we seek to demonstrate that LSTM's performance remains robust even as experimental conditions deteriorate. Specifically, we show that classification accuracy exhibits graceful degradation when the LSTM network is faced with (a) lower quantities of available training data, (b) tighter deadlines for decision making (i.e., shorter available input data sequences) and (c) poorer video quality (resulting from noise, dropped frames or reduced resolution). We also clearly demonstrate the benefits of memory for video processing, particularly, under high noise or frame drop rates. Our study is thus an initial step towards demonstrating LSTM's potential for robust action recognition in real-world scenarios.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"63 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":"116276676","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: 67

Memristor-based synapse design and a case study in reconfigurable systems 基于忆阻器的突触设计与可重构系统的案例研究

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706776

Feng Ji, Hai Helen Li, B. Wysocki, C. Thiem, N. McDonald

引用次数: 2

A spiking thalamus model for form and motion processing of images 一种用于图像形式和运动处理的尖峰丘脑模型

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706790

Suhas E. Chelian, N. Srinivasa

引用次数: 2

Kernel-based distance metric learning in the output space 输出空间中基于核的距离度量学习

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-01 DOI: 10.1109/IJCNN.2013.6706862

Cong Li, M. Georgiopoulos, G. Anagnostopoulos

引用次数: 6