2016 International Joint Conference on Neural Networks (IJCNN)最新文献

A comparison of action selection methods for implicit policy method reinforcement learning in continuous action-space 连续动作空间中隐式策略方法强化学习的动作选择方法比较

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-11-03 DOI: 10.1109/IJCNN.2016.7727688

Barry D. Nichols

引用次数: 5

An experimental evaluation of echo state network for colour image segmentation 回声状态网络在彩色图像分割中的实验评价

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-11-03 DOI: 10.1109/IJCNN.2016.7727326

Abdelkerim Souahlia, A. Belatreche, A. Benyettou, K. Curran

{"title":"An experimental evaluation of echo state network for colour image segmentation","authors":"Abdelkerim Souahlia, A. Belatreche, A. Benyettou, K. Curran","doi":"10.1109/IJCNN.2016.7727326","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727326","url":null,"abstract":"Image segmentation refers to the process of dividing an image into multiple regions which represent meaningful areas. Image segmentation is an essential step for most image analysis tasks such as object recognition and tracking, pattern recognition, content-based image retrieval, etc. In recent years, a large number of image segmentation algorithms have been developed, but achieving accurate segmentation still remains a challenging task. Recently, reservoir computing (RC) has drawn much attention in machine learning as a new model of recurrent neural networks (RNN). Echo State Network (ESN) represents one efficient realization of RC, which is initially designed to facilitate learning in Recurrent Neural Networks. In this paper we investigate the viability of ESN as feature extractor for pixel classification based colour image segmentation. Extensive experiments are conducted on real world colour image datasets and the global ESN reservoir parameters are varied to identify their operating ranges that allow the use of the reservoir nodes internal activations as new pixel features for the colour image segmentation task. A simple feed forward neural network is used to realize the ESN readout function and classify these new features. The experimental results show that the proposed method achieves high performance image segmentation comparing with state-of-the-art techniques. In addition, a set of empirically derived guidelines for setting the reservoir global parameters are proposed.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"121 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129408520","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

Diverse, noisy and parallel: a New Spiking Neural Network approach for humanoid robot control 多元、噪声和并行:一种新的脉冲神经网络方法用于仿人机器人控制

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-11-03 DOI: 10.1109/IJCNN.2016.7727325

Ricardo de Azambuja, A. Cangelosi, S. Adams

引用次数: 13

Non-parametric Hidden Conditional Random Fields for action classification 用于动作分类的非参数隐藏条件随机场

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-11-03 DOI: 10.1109/IJCNN.2016.7727615

Natraj Raman, S. Maybank

{"title":"Non-parametric Hidden Conditional Random Fields for action classification","authors":"Natraj Raman, S. Maybank","doi":"10.1109/IJCNN.2016.7727615","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727615","url":null,"abstract":"Conditional Random Fields (CRF), a structured prediction method, combines probabilistic graphical models and discriminative classification techniques in order to predict class labels in sequence recognition problems. Its extension the Hidden Conditional Random Fields (HCRF) uses hidden state variables in order to capture intermediate structures. The number of hidden states in an HCRF must be specified a priori. This number is often not known in advance. A non-parametric extension to the HCRF, with the number of hidden states automatically inferred from data, is proposed here. This is a significant advantage over the classical HCRF since it avoids ad hoc model selection procedures. Further, the training and inference procedure is fully Bayesian eliminating the over fitting problem associated with frequentist methods. In particular, our construction is based on scale mixtures of Gaussians as priors over the HCRF parameters and makes use of Hierarchical Dirichlet Process (HDP) and Laplace distribution. The proposed inference procedure uses elliptical slice sampling, a Markov Chain Monte Carlo (MCMC) method, in order to sample optimal and sparse posterior HCRF parameters. The above technique is applied for classifying human actions that occur in depth image sequences - a challenging computer vision problem. Experiments with real world video datasets confirm the efficacy of our classification approach.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124485096","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: 5

Learning perceptual texture similarity and relative attributes from computational features 从计算特征中学习感知纹理相似度和相关属性

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-11-03 DOI: 10.1109/IJCNN.2016.7727516

Jianwen Lou, Lin Qi, Junyu Dong, Hui Yu, G. Zhong

引用次数: 4

Verification of fraudulent PIN holders by brain waves 通过脑电波验证欺诈性密码持有人

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-10-31 DOI: 10.1109/IJCNN.2016.7727454

Hiromichi Iwase, T. Horie, Y. Matsuyama

{"title":"Verification of fraudulent PIN holders by brain waves","authors":"Hiromichi Iwase, T. Horie, Y. Matsuyama","doi":"10.1109/IJCNN.2016.7727454","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727454","url":null,"abstract":"Brain waves, or electroencephalograms (EEGs), are applicable to user verification. We devise a two-factor system so that impersonators who hold identification numbers in fraudulence are detectable. In the first step, a subject either authentic or false tries to input a digit of a ten-key in the personal identification number by a P300 speller. The P300 speller is a brain-computer interface that detects positive voltage jump when a subject identifies specific digits on a display visually. By considering the performance of the P300 speller, we allow an error of one digit out of the four digits. On the other hand, we keep suspicion even for the case of perfect four digits because of the possibility of impersonation by a stolen case. Following the P300 spelling, we apply a verification of subjects by brain waves. Averaging of detected P300 waveforms after band-pass filtering takes the role of feature extraction. Then, a support vector machine applied to the averaged waveforms decides whether the subject is authentic or false. Thus, the total system does not entail the complexity of multimodality. For this system, we measured average error rates for 20 subjects. Experiments showed the false rejection rate of 3.9% at the false acceptance rate of 0% for the 4-digit number case. These pair values are successfully low even by using brain waves that usually contain many artifacts. Additionally, experiments on a diabetes patient before and after an insulin injection are also conducted. The result shows that the appropriate injection control maintains no difference from ordinary subjects. In concluding remarks, we consider methods to increase subjects and digits for applications in a larger society.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"71 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131456617","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

A deep quasi-linear kernel composition method for support vector machines 支持向量机的深度拟线性核组合方法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-10-31 DOI: 10.1109/IJCNN.2016.7727394

Weite Li, Jinglu Hu, Benhui Chen

引用次数: 2

Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework 利用神经工程框架高效模拟三角帆的异联想记忆

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-10-31 DOI: 10.1109/IJCNN.2016.7727888

James C. Knight, Aaron R. Voelker, Andrew Mundy, C. Eliasmith, S. Furber

{"title":"Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework","authors":"James C. Knight, Aaron R. Voelker, Andrew Mundy, C. Eliasmith, S. Furber","doi":"10.1109/IJCNN.2016.7727888","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727888","url":null,"abstract":"The biological brain is a highly plastic system within which the efficacy and structure of synaptic connections are constantly changing in response to internal and external stimuli. While numerous models of this plastic behavior exist at various levels of abstraction, how these mechanisms allow the brain to learn meaningful values is unclear. The Neural Engineering Framework (NEF) is a hypothesis about how large-scale neural systems represent values using populations of spiking neurons, and transform them using functions implemented by the synaptic weights between populations. By exploiting the fact that these connection weight matrices are factorable, we have recently shown that static NEF models can be simulated very efficiently using the SpiNNaker neuromorphic architecture. In this paper, we demonstrate how this approach can be extended to efficiently support both supervised and unsupervised learning rules designed to operate on these factored matrices. We then present a heteroassociative memory architecture built using these learning rules and prove that it is capable of learning a human-scale semantic network. Finally we demonstrate a 100 000 neuron version of this architecture running on the SpiNNaker simulator with a speed-up exceeding 150x when compared to the Nengo reference simulator.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133290756","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: 19

Wind ramp event prediction with parallelized gradient boosted regression trees 并行梯度增强回归树的风坡道事件预测

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-10-17 DOI: 10.1109/IJCNN.2016.7727900

Saurav Gupta, N. Shrivastava, A. Khosravi, B. K. Panigrahi

引用次数: 10

Deep Random Vector Functional Link Network for handwritten character recognition 用于手写字符识别的深度随机向量功能链接网络

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-10-03 DOI: 10.1109/IJCNN.2016.7727666

H. Cecotti

{"title":"Deep Random Vector Functional Link Network for handwritten character recognition","authors":"H. Cecotti","doi":"10.1109/IJCNN.2016.7727666","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727666","url":null,"abstract":"The field of artificial neural networks has a long history of several decades, where the theoretical contributions have progressed with advances in terms of power and memory in present day computers. Some old methods are now rebranded or represented, taking advantage of the power of present day computers. More particularly, we consider the current trend of Random Vector Functional Link Networks, which suggests that the architecture of a system and the learning algorithm should be properly decoupled. In this paper, we evaluate the performance of multi-layers Random Vector Functional Link Network (RVFL)/ extreme machine learning (EML) on four databases of handwritten characters. Particularly, we evaluate the impact of the architecture (number of neurons per hidden layer), and the robustness of the distribution of the results across different runs. By combining the classifier outputs from different runs, we show that such a maximum combination rule provides an accuracy of 95.97% for Arabic digits, 98.03% for Bangla, 98.64% for Devnagari, and 96.30% for Oriya digits. The results confirm that increasing the size of the hidden layers has a significant impact on the accuracy, and allows to reach state-of-the-art performance; however the performance reaches a plateau after a certain size of the hidden layers.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131917490","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: 16