The 2011 International Joint Conference on Neural Networks最新文献_第5页

Learning algorithms for a specific configuration of the quantron 学习算法的特定配置的量子

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033271

S. Montigny, Richard Labib

引用次数: 1

Forecasting tropospheric ozone concentrations with adaptive neural networks 自适应神经网络预测对流层臭氧浓度

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033450

Riccardo Taormina, L. Mesin, Fiammetta Orione, E. Pasero

引用次数: 1

Recognition model of cerebral cortex based on approximate belief revision algorithm 基于近似信念修正算法的大脑皮层识别模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033247

Yuuji Ichisugi

{"title":"Recognition model of cerebral cortex based on approximate belief revision algorithm","authors":"Yuuji Ichisugi","doi":"10.1109/IJCNN.2011.6033247","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033247","url":null,"abstract":"We propose a computational model of recognition of the cerebral cortex, based on an approximate belief revision algorithm. The algorithm calculates the MPE (most probable explanation) of Bayesian networks with a linear-sum CPT (conditional probability table) model. Although the proposed algorithm is simple enough to be implemented by a fixed circuit, results of the performance evaluation show that this algorithm does not have bad approximation accuracy. The mean convergence time is not sensitive to the number of nodes if the depth the network is constant. The computation amount is linear to the number of nodes if the number of edges per node is constant. The proposed algorithm can be used as a part of a learning algorithm for a kind of sparse-coding, which reproduces orientation selectivity of the primary visual area. The circuit that executes the algorithm shows better correspondence to the anatomical structure of the cerebral cortex, namely its six-layer and columnar features, than the approximate belief propagation algorithm that has been proposed before. These results suggest that the proposed algorithm is a promising starting point for the model of the recognition mechanism of the cerebral cortex.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132612651","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: 6

Parameter selection for smoothing splines using Stein's Unbiased Risk Estimator 基于Stein无偏风险估计的光滑样条参数选择

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033577

Sepideh Seifzadeh, Mohammad Rostami, A. Ghodsi, F. Karray

引用次数: 5

Coherence vector of Oriented Gradients for traffic sign recognition using Neural Networks 面向梯度的相干向量神经网络交通标志识别

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033318

Reghunadhan Rajesh, K. Rajeev, K. Suchithra, L. V. Prabhu, Vignesh Gopakumar, N. Ragesh

引用次数: 21

Composite power system reliability evaluation using support vector machines on a multicore platform 多核平台上基于支持向量机的复合电力系统可靠性评估

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033556

R. Green, Lingfeng Wang, Mansoor Alam

{"title":"Composite power system reliability evaluation using support vector machines on a multicore platform","authors":"R. Green, Lingfeng Wang, Mansoor Alam","doi":"10.1109/IJCNN.2011.6033556","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033556","url":null,"abstract":"Monte Carlo Simulation (MCS) is a very powerful and flexible tool when used for sampling states during the probabilistic reliability assessment of power systems. Despite the advantages of MCS, the method begins to falter when applied to large and more complex systems of higher dimensions. In these cases it is often the process of classifying states that consumes the majority of computational time and resources. This is especially true in power systems reliability evaluation where the main method of classification is typically an Optimal Power Flow (OPF) formulation in the form of a linear program (LP). Previous works have improved the computational time required for classification by using Neural Networks (NN) of varying types in place of the OPF. A method of classification that is lighter weight and often more computationally efficient than NNs is the Support Vector Machine (SVM). This work couples SVM with the MCS algorithm in order to improve the computational time of classification and overall reliability evaluation. The method is further extended through the use of a multi-core architecture in order to further decrease computational time. These formulations are tested using the IEEE Reliability Test Systems (IEEE-RTS79 and IEEE-RTS96). Significant improvements in computational time are demonstrated while a high level of accuracy is maintained.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131244653","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

Topic model with constrainted word burstiness intensities 具有约束突发性词强度的主题模型

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033201

Shaoze Lei, Jianwen Zhang, Shifeng Weng, Changshui Zhang

{"title":"Topic model with constrainted word burstiness intensities","authors":"Shaoze Lei, Jianwen Zhang, Shifeng Weng, Changshui Zhang","doi":"10.1109/IJCNN.2011.6033201","DOIUrl":"https://doi.org/10.1109/IJCNN.2011.6033201","url":null,"abstract":"Word burstiness phenomenon, which means that if a word occurs once in a document it is likely to occur repeatedly, has interested the text analysis field recently. Dirichlet Compound Multinomial Latent Dirichlet Allocation (DCMLDA) introduces this word burstiness mechanism into Latent Dirichlet Allocation (LDA). However, in DCMLDA, there is no restriction on the word burstiness intensity of each topic. Consequently, as shown in this paper, the burstiness intensities of words in major topics will become extremely low and the topics' ability to represent different semantic meanings will be impaired. In order to get topics that represent semantic meanings of documents well, we introduce constraints on topics' word burstiness intensities. Experiments demonstrate that DCMLDA with constrained word burstiness intensities achieves better performance than the original one without constraints. Besides, these additional constraints help to reveal the relationship between two key properties inherited from DCM and LDA respectively. These two properties have a great influence on the combined model's performance and their relationship revealed by this paper is an important guidance for further study of topic models.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132816638","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 novel asynchronous digital spiking neuron model and its various neuron-like bifurcations and responses 一种新的异步数字尖峰神经元模型及其各种神经元样分叉和响应

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033295

Takashi Matsubara, H. Torikai

引用次数: 8

Bio-inspired balanced tree structure dynamic network 仿生平衡树形结构动态网络

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033225

Fengchen Liu, Yongsheng Ding, Weixun Gao

引用次数: 0

Instance selection algorithm based on a Ranking Procedure 基于排序过程的实例选择算法

The 2011 International Joint Conference on Neural Networks Pub Date : 2011-10-03 DOI: 10.1109/IJCNN.2011.6033531

C. S. Pereira, George D. C. Cavalcanti

引用次数: 7