The 2012 International Joint Conference on Neural Networks (IJCNN)最新文献_第10页

Transfer learning for Latin and Chinese characters with Deep Neural Networks 基于深度神经网络的拉丁文和中文字符迁移学习

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252544

D. Ciresan, U. Meier, J. Schmidhuber

引用次数: 207

Particle filters and beamforming for EEG source estimation 脑电信号源估计的粒子滤波和波束形成

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252516

P. Georgieva, L. Mihaylova, N. Bouaynaya, L. Jain

引用次数: 5

A neuroscientific approach to choice modeling: Electroencephalogram (EEG) and user preferences 选择建模的神经科学方法:脑电图(EEG)和用户偏好

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252561

R. Khushaba, S. Kodagoda, G. Dissanayake, Luke Greenacre, Sandra Burke, J. Louviere

{"title":"A neuroscientific approach to choice modeling: Electroencephalogram (EEG) and user preferences","authors":"R. Khushaba, S. Kodagoda, G. Dissanayake, Luke Greenacre, Sandra Burke, J. Louviere","doi":"10.1109/IJCNN.2012.6252561","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252561","url":null,"abstract":"Discrete choice experiments have traditionally focused on improving the prediction of static choices that are measured through external reflection and surveys. It is argued that considering the underlying processes of decision making across a variety of contexts may further progress decision research. As a pilot study in this field, this paper explores the dynamic nature of decision-making by examining the associated brain activity, Electroencephalogram (EEG), of people while undertaking choices designed to elicit their preferences. To facilitate such a study, the Tobii-Studio eye tracker system was utilized to capture the participants' choice based preferences when they were observing seventy two sets of objects of three images offering potential personal computer backgrounds. Choice based preferences were identified by having the respondent click on their preferred image. In addition, the commercial Emotiv wireless EEG headset with 14 channels was utilized to capture the associated brain activity during the period of the experiments. Principal Component Analysis (PCA) was utilized to preprocess the EEG data before analyzing it with the Fast Fourier Transform (FFT) to observe the changes in the four principal frequency bands, theta (3 - 7 Hz), alpha (8 - 12 Hz), beta (13 - 30 Hz), and gamma (30 - 40 Hz). A mutual information (MI) measure was then used to study left-to-right hemisphere differences as well as front-to-back difference. Across six recruited participants there was a clear and significant change in the spectral activities taking place mainly in the frontal (theta and alpha across F3 and F4) and occipital (alpha and beta across O1 and O2) regions while the participants were indicating their preferences.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116603077","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

Predicting protein-ligand binding site with differential evolution and support vector machine 基于差分进化和支持向量机的蛋白质配体结合位点预测

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/ijcnn.2012.6252744

Ginny Y. Wong, Frank H. F. Leung, Sai Ho Ling

引用次数: 3

Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning 利用广度集成学习从1H-MRS数据中分类恶性脑肿瘤

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252756

A. Vilamala, L. B. Muñoz, A. Vellido

{"title":"Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning","authors":"A. Vilamala, L. B. Muñoz, A. Vellido","doi":"10.1109/IJCNN.2012.6252756","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252756","url":null,"abstract":"In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive measurement methods, the most common of which resort to imaging techniques. The discrimination between high-grade malignant tumours of different origin but similar characteristics, such as glioblastomas and metastases, is a particularly difficult problem in this context. This is because imaging techniques are often not sensitive enough and their spectroscopic signal is overall too similar. In spite of this, machine learning techniques, coupled with robust feature selection procedures, have recently made substantial inroads into the problem. In this study, magnetic resonance spectroscopy data from an international, multi-centre database were used to discriminate between these two types of malignant brain tumours using ensemble learning techniques, with a focus on the definition of a feature selection method specifically designed for ensembles. This method, Breadth Ensemble Learning, takes advantage of the fact that many of the frequencies of the available spectra convey no relevant information for the discrimination of the tumours. The potential of the proposed method is supported by some of the best results reported to date for this problem.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121856198","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

A spectral algorithm for topographical Co-clustering 地形共聚类的光谱算法

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252398

Nicoleta Rogovschi, Lazhar Labiod, M. Nadif

引用次数: 3

Deep, super-narrow neural network is a universal classifier 深度、超窄神经网络是一种通用分类器

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252513

Lech Szymanski, B. McCane

引用次数: 8

Analysis of pedestrian spatial behaviour using GDTW-P-SVMs 基于gdtw - p - svm的行人空间行为分析

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252584

A. Jalalian, S. Chalup, Michael J. Ostwald

{"title":"Analysis of pedestrian spatial behaviour using GDTW-P-SVMs","authors":"A. Jalalian, S. Chalup, Michael J. Ostwald","doi":"10.1109/IJCNN.2012.6252584","DOIUrl":"https://doi.org/10.1109/IJCNN.2012.6252584","url":null,"abstract":"This paper presents an analysis system to find the impact of architectural designs on pedestrian behavioural data. The system employs GDTW-P-SVMs which are capable of modelling sequential data with variable-length input series. We apply GDTW-P-SVMs to simulated pedestrian spatial behaviour data. The data include four types of behavioural characteristics: i) movement trajectories, ii) walking speed, iii) the angle α between the movement vector and the gaze vector and iv) its derivative. The analysis system learns a statistical model characterising three classes of spatial behaviour. The classes are formed based on pedestrians' reactions to visual attractions in a simulated environment. A separate data set that includes the crowd attraction effect is used to discuss the impact of social group formation on the classification result. Our experiments show that using the angle α and its derivative as input to the classifiers results in lower classification error rates compared to classification of trajectory and speed of movement data. We compare the classification accuracy of the GDTW-P-SVMs with other classification methods that are capable of handling data objects with variable-length input series. GDTW-P-SVMs showed promising results in classifying the simulated behavioural data.","PeriodicalId":287844,"journal":{"name":"The 2012 International Joint Conference on Neural Networks (IJCNN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134303619","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

Autonomous learning of collaboration among robots 机器人之间协作的自主学习

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252664

P. Arena, L. Patané, A. Vitanza

引用次数: 2

Data driven fitting sample selection for time series forecasting with neural networks 神经网络时间序列预测的数据驱动拟合样本选择

The 2012 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2012-06-10 DOI: 10.1109/IJCNN.2012.6252528

N. Kourentzes

引用次数: 2