The 2013 International Joint Conference on Neural Networks (IJCNN)最新文献

Improving multi-label classification performance by label constraints 通过标签约束改进多标签分类性能

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-12-01 DOI: 10.1109/IJCNN.2013.6706861

Benhui Chen, Xuefen Hong, Lihua Duan, Jinglu Hu

引用次数: 10

Spiking neural networks for financial data prediction 用于金融数据预测的脉冲神经网络

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-12-01 DOI: 10.1109/IJCNN.2013.6707140

D. Reid, A. Hussain, H. Tawfik

引用次数: 14

An SVM-based approach for stock market trend prediction 基于支持向量机的股票市场趋势预测方法

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-12-01 DOI: 10.1109/IJCNN.2013.6706743

Yuling Lin, Haixiang Guo, Jinglu Hu

{"title":"An SVM-based approach for stock market trend prediction","authors":"Yuling Lin, Haixiang Guo, Jinglu Hu","doi":"10.1109/IJCNN.2013.6706743","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706743","url":null,"abstract":"In this paper, an SVM-based approach is proposed for stock market trend prediction. The proposed approach consists of two parts: feature selection and prediction model. In the feature selection part, a correlation-based SVM filter is applied to rank and select a good subset of financial indexes. And the stock indicators are evaluated based on the ranking. In the prediction model part, a so called quasi-linear SVM is applied to predict stock market movement direction in term of historical data series by using the selected subset of financial indexes as the weighted inputs. The quasi-linear SVM is an SVM with a composite quasi-linear kernel function, which approximates a nonlinear separating boundary by multi-local linear classifiers with interpolation. Experimental results on Taiwan stock market datasets demonstrate that the proposed SVM-based stock market trend prediction method produces better generalization performance over the conventional methods in terms of the hit ratio. Moreover, the experimental results also show that the proposed SVM-based stock market trend prediction system can find out a good subset and evaluate stock indicators which provide useful information for investors.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475530","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: 114

Biologically inspired intensity and range image feature extraction 生物启发的强度和距离图像特征提取

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-08 DOI: 10.1109/IJCNN.2013.6706968

D. Kerr, S. Coleman, T. McGinnity, Marine Clogenson

{"title":"Biologically inspired intensity and range image feature extraction","authors":"D. Kerr, S. Coleman, T. McGinnity, Marine Clogenson","doi":"10.1109/IJCNN.2013.6706968","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706968","url":null,"abstract":"The recent development of low cost cameras that capture 3-dimensional images has changed the focus of computer vision research from using solely intensity images to the use of range images, or combinations of RGB, intensity and range images. The low cost and widespread availability of the hardware to capture these images has realised many possible applications in areas such as robotics, object recognition, surveillance, manipulation, navigation and interaction. Given the large volumes of data in range images, processing and extracting the relevant information from the images in real time becomes challenging. To achieve this, much research has been conducted in the area of bio-inspired feature extraction which aims to emulate the biological processes used to extract relevant features, reduce redundancy, and process images efficiently. Inspired by the behaviour of biological vision systems, an approach is presented for extracting important features from intensity and range images, using biologically inspired spiking neural networks in order to model aspects of the functional computational capabilities of the visual system.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122679400","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

Power analysis of large-scale, real-time neural networks on SpiNNaker SpiNNaker上大规模实时神经网络的功率分析

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706927

Evangelos Stromatias, F. Galluppi, Cameron Patterson, S. Furber

{"title":"Power analysis of large-scale, real-time neural networks on SpiNNaker","authors":"Evangelos Stromatias, F. Galluppi, Cameron Patterson, S. Furber","doi":"10.1109/IJCNN.2013.6706927","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706927","url":null,"abstract":"Simulating large spiking neural networks is non trivial: supercomputers offer great flexibility at the price of power and communication overheads; custom neuromorphic circuits are more power efficient but less flexible; while alternative approaches based on GPGPUs and FPGAs, whilst being more readily available, show similar model specialization. As well as efficiency and flexibility, real time simulation is a desirable neural network characteristic, for example in cognitive robotics where embodied agents interact with the environment using low-power, event-based neuromorphic sensors. The SpiNNaker neuromimetic architecture has been designed to address these requirements, simulating large-scale heterogeneous models of spiking neurons in real-time, offering a unique combination of flexibility, scalability and power efficiency. In this work a 48-chip board is utilised to generate a SpiNNaker power estimation model, based on numbers of neurons, synapses and their firing rates. In addition, we demonstrate simulations capable of handling up to a quarter of a million neurons, 81 million synapses and 1.8 billion synaptic events per second, with the most complex simulations consuming less than 1 Watt per SpiNNaker chip.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401781","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: 84

Quantum neural network based surface EMG signal filtering for control of robotic hand 基于量子神经网络的表面肌电信号滤波在机械手控制中的应用

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706781

Vaibhav Gandhi, T. Mcginnity

{"title":"Quantum neural network based surface EMG signal filtering for control of robotic hand","authors":"Vaibhav Gandhi, T. Mcginnity","doi":"10.1109/IJCNN.2013.6706781","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706781","url":null,"abstract":"A filtering methodology inspired by the principles of quantum mechanics and incorporating the well-known Schrodinger wave equation is investigated for the first time for filtering EMG signals. This architecture, referred to as a Recurrent Quantum Neural Network (RQNN) can characterize a non-stationary stochastic signal as time varying wave packets. An unsupervised learning rule allows the RQNN to capture the statistical behaviour of the input signal and facilitates estimation of an EMG signal embedded in noise with unknown characteristics. Results from a number of benchmark tests show that simple signals such as DC, staircase DC and sinusoidal signals embedded with a high level of noise can be accurately filtered. Particle swarm optimization is employed to select RQNN model parameters for filtering simple signals. In this paper, we present the RQNN filtering procedure, using heuristically selected parameters, to be applied to a new thirteen class EMG based finger movement detection system, for emulation in a Shadow Robotics robot hand. It is shown that the RQNN EMG filtering improves the classification performance compared to using only the raw EMG signals, across multiple feature extraction approaches and subjects. Effective control of the robot hand is demonstrated.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134592368","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: 13

Modeling populations of spiking neurons for fine timing sound localization 尖峰神经元群体的精细定时声音定位建模

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706931

Qian Liu, Cameron Patterson, S. Furber, Zhangqin Huang, Yibin Hou, Huibing Zhang

引用次数: 6

A location-independent direct link neuromorphic interface 一个与位置无关的直连神经形态接口

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6706887

Alexander D. Rast, J. Partzsch, C. Mayr, J. Schemmel, Stefan Hartmann, L. Plana, S. Temple, D. Lester, R. Schüffny, S. Furber

{"title":"A location-independent direct link neuromorphic interface","authors":"Alexander D. Rast, J. Partzsch, C. Mayr, J. Schemmel, Stefan Hartmann, L. Plana, S. Temple, D. Lester, R. Schüffny, S. Furber","doi":"10.1109/IJCNN.2013.6706887","DOIUrl":"https://doi.org/10.1109/IJCNN.2013.6706887","url":null,"abstract":"With neuromorphic hardware rapidly moving towards large-scale, possibly immovable systems capable of implementing brain-scale neural models in hardware, there is an emerging need to be able to integrate multi-system combinations of sensors and cortical processors over distributed, multisite configurations. If there were a standard, direct interface allowing large systems to communicate using native signalling, it would be possible to use heterogeneous resources efficiently according to their task suitability. We propose a UDP-based AER spiking interface that permits direct bidirectional spike communications over standard networks, and demonstrate a practical implementation with two large-scale neuromorphic systems, BrainScaleS and SpiNNaker. Internally, the interfaces at either end appear as interceptors which decode and encode spikes in a standardised AER address format onto UDP frames. The system is able to run a spiking neural network distributed over the two systems, in both a side-by-side setup with a direct cable link and over the Internet between 2 widely spaced sites. Such a model not only realises a solution for connecting remote sensors or processors to a large, central neuromorphic simulation platform, but also opens possibilities for interesting automated remote neural control, such as parameter tuning, for large, complex neural systems, and suggests methods to overcome differences in timescale and simulation model between different platforms. With its entirely standard protocol and physical layer, the interface makes large neuromorphic systems a distributed, accessible resource available to all.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121539314","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: 18

Dealing with highly imbalanced textual data gathered into similar classes 处理收集到类似类中的高度不平衡的文本数据

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6707044

Jean-Charles Lamirel

引用次数: 1

Parallel incremental SVM for classifying million images with very high-dimensional signatures into thousand classes 基于并行增量支持向量机的高维图像分类算法

The 2013 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2013-08-04 DOI: 10.1109/IJCNN.2013.6707121

Thanh-Nghi Doan, Thanh-Nghi Do, F. Poulet

引用次数: 4