2007 International Joint Conference on Neural Networks最新文献_第6页

A hybrid Support Vector Machine and autoregressive model for detecting gait disorders in the elderly 基于混合支持向量机和自回归模型的老年人步态障碍检测

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370976

D. Lai, A. Khandoker, R. Begg, M. Palaniswami

{"title":"A hybrid Support Vector Machine and autoregressive model for detecting gait disorders in the elderly","authors":"D. Lai, A. Khandoker, R. Begg, M. Palaniswami","doi":"10.1109/IJCNN.2007.4370976","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4370976","url":null,"abstract":"The consequence of tripping and falling in the elderly population is serious because of the life threatening fractures which occur and the high medical costs incurred. Recently, the minimum toe clearance (MTC) has been employed in gait analysis as a sensitive gait variable for early detection of elderly people at risk of falling. In previous work, we successfully applied statistical and wavelet analysis methods with Support Vector Machines (SVM) to model the risk of tripping in the elderly. In this work, we propose to model the MTC time series as a wide based stationary random signal using the autoregressive (AR) process. Initially, it was found that a fourth order AR model constructed from 512 MTC samples per subject on 23 subjects completely modelled the balance impaired gait (pathological) from normal gait. However, when the number of MTC samples were reduced to 32, the two groups became inseparable. We then proposed a hybrid system consisting of a SVM classifier with AR model coefficients as input features to separate the two classes. It was found that SVMs with linear and Gaussian kernels produced 100% leave one out accuracies without the need for prior feature selection algorithms. In contrast, SVM models built previously from the best set of wavelet features produced only 86.95% leave one out accuracies. These results suggest that pathological gait is best modelled by the AR process if sufficient MTC data is available. In the case of shorter MTC data, the AR model still provides powerful and robust discriminative features which can be used by the SVM to detect elderly people at risk of falling.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132434817","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

An Associative Memory for Association Rule Mining 一种用于关联规则挖掘的关联内存

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371304

Vicente Oswaldo Baez Monroy, Simon E. M. O'Keefe

引用次数: 6

Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier 基于协同多面向分类器的水下目标分类

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371232

J. Cartmill, M. Azimi-Sadjadi, N. Wachowski

引用次数: 15

Intelligent Tracking Controller Design Using Dynamic Fuzzy Neural Networks 基于动态模糊神经网络的智能跟踪控制器设计

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370946

Chun-Fei Hsu, Tsu-Tian Lee, Ping-Zong Lin

引用次数: 4

Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines 核机设计中的不可知论学习与先验知识

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371219

G. Cawley, N. L. C. Talbot

引用次数: 9

Pseudo RBF Network for Position Independent Hand Posture Recognition System 位置无关手势识别系统的伪RBF网络

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371103

H. Hikawa, Shigeki Matsubara

引用次数: 8

Intuitive Clustering of Biological Data 生物数据的直观聚类

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371244

B. Hammer, A. Hasenfuss, Frank-Michael Schleif, T. Villmann, M. Strickert, U. Seiffert

引用次数: 4

Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer 人工神经网络的事件时间分析:乳腺癌的综合分析和基于规则的研究

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371357

P. Lisboa, S. Bonnevay, S. Négrier, T. Etchells, I. Jarman, M.S. Hane Aung, S. Chabaud, T. Bachelot, D. Pérol, T. Gargi, V. Bourdès

{"title":"Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer","authors":"P. Lisboa, S. Bonnevay, S. Négrier, T. Etchells, I. Jarman, M.S. Hane Aung, S. Chabaud, T. Bachelot, D. Pérol, T. Gargi, V. Bourdès","doi":"10.1109/IJCNN.2007.4371357","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371357","url":null,"abstract":"This paper presents an analysis of censored survival data for breast cancer specific mortality and disease free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit in using the neural network framework is better specificity for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing and by comparing marginal estimates of the predicted and actual cumulative hazards. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115463769","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: 27

Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds 用于分子化合物视觉探索的自组织图谱快速训练

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371399

A. Fiannaca, G. D. Fatta, R. Rizzo, A. Urso, S. Gaglio

引用次数: 0

Off-line Signature Verification Using Writer-Independent Approach 使用独立于写者的方法进行离线签名验证

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371358

Luiz Oliveira, E. Justino, R. Sabourin

引用次数: 43