2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)最新文献

A group emotion control system based on reinforcement learning 基于强化学习的群体情绪控制系统

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492826

Kee-Hoon Kim, Sung-Bae Cho

引用次数: 3

Improving an adaptive differential evolution using hill-valley detection 利用山谷检测改进自适应差分进化

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.3233/HIS-160220

T. Takahama, S. Sakai

{"title":"Improving an adaptive differential evolution using hill-valley detection","authors":"T. Takahama, S. Sakai","doi":"10.3233/HIS-160220","DOIUrl":"https://doi.org/10.3233/HIS-160220","url":null,"abstract":"Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multi-modal functions. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on parameter control is JADE. In JADE, two parameter values are generated according to a probability density function which is learned by the parameter values in success cases, where the child is better than the parent. In this study, landscape of an objective function is paid attention to in order to improve the performance of JADE. The efficiency and robustness of search process can be improved by detecting valleys and hills in search points and by adopting a small F for valley points and a large F for hill points because an optimal solution exists near valleys and far from hills in minimization problems. Valley points and hill points are detected by creating a proximity graph from search points and by selecting valley/hill points that are smaller/greater than neighbor points. The effect of the proposed method is shown by solving thirteen benchmark problems.","PeriodicalId":409493,"journal":{"name":"2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125563580","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

Global, local and embedded architectures for multiclass classification with foreign elements rejection: An overview 排除外来元素的多类分类的全局、局部和嵌入式体系结构综述

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492789

W. Homenda, A. Jastrzębska

{"title":"Global, local and embedded architectures for multiclass classification with foreign elements rejection: An overview","authors":"W. Homenda, A. Jastrzębska","doi":"10.1109/SOCPAR.2015.7492789","DOIUrl":"https://doi.org/10.1109/SOCPAR.2015.7492789","url":null,"abstract":"In the paper we look closely at the issue of contaminated data sets, where apart from proper elements we may have garbage. In a typical scenario, further classification of such data sets is always negatively influenced by garbage elements. Ideally, we would like to remove them from the data set entirely. Garbage elements are called here foreign elements and the task of removing them from the data set is called rejection of foreign elements. The paper is devoted to comparison and analysis of three different models capable to perform classification with rejection of foreign elements. It shall be emphasized that all studied methods are based only on proper patterns and no knowledge about foreign elements is needed to construct them. Hence, the methods we study are truly general and could be applied in many ways and in many problems. The following classification/rejection architectures are considered: global, local, and embedded. We analyze their performance in two aspects: influence of rejection mechanisms on classification and the quality of rejection. Issues are addressed theoretically and empirically in a study of handwritten digits recognition. Results show that the local architecture and the embedded architecture are advantageous, in comparison to the global architecture.","PeriodicalId":409493,"journal":{"name":"2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633695","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: 4

Solving the obstacle neutralization problem using swarm intelligence algorithms 用群智能算法求解障碍物中和问题

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492805

Ramazan Algin, A. F. Alkaya

引用次数: 3

Facial action units detection by robust temporal features 基于鲁棒时间特征的面部动作单元检测

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492801

Prarinya Siritanawan, K. Kotani

引用次数: 4

Biography commercial serial crime analysis using enhanced dynamic neural network 基于增强动态神经网络的传记商业系列犯罪分析

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492769

A. Ghazvini, M. Nazri, S. Abdullah, Md Nawawi Junoh, Zainal Abidin bin Kasim

{"title":"Biography commercial serial crime analysis using enhanced dynamic neural network","authors":"A. Ghazvini, M. Nazri, S. Abdullah, Md Nawawi Junoh, Zainal Abidin bin Kasim","doi":"10.1109/SOCPAR.2015.7492769","DOIUrl":"https://doi.org/10.1109/SOCPAR.2015.7492769","url":null,"abstract":"In sphere of criminology, suspect prediction analysis has been the point of convergence for many researchers. The focus of this study is on three prime attributes of next serial suspect's biography including nationality, age and time. Generally, to prevent the uncertainty in dynamic systems by nonlinear methods, a predictor is required in Time Delay Neural Network (TDNN). However, existing TDNN with single activation function is less effective to predict labeled class due to lower accuracy. Poor approximation of smooth mapping in single hidden layer makes it less effective. This study aims to propose a combined transfer functions to improve Nonlinear Autoregressive Time Series for performance prediction with exogenous (external) input (NARX)'s by utilizing Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. Consequently Hyperbolic Tangent Sigmoid (Tansig) and Radial Basis Function (RBF) are used in LM and SCG algorithms as bi-transfer functions for prediction of next suspect's biography in commercial serial case. The results of NARX model with combination of Tansig and RBF as two objective of transfer functions of LM and SCG, presented better performance for prediction of next serial crime suspect's biography in comparison to single activation function of Tansig and RBF.","PeriodicalId":409493,"journal":{"name":"2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132133402","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

Patch based inpainting method based on the F1-transform 基于f1变换的基于Patch的补图方法

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492813

Pavel Vlasánek, I. Perfilieva

引用次数: 1

Detecting spliced face in a group photo using PCA 基于PCA的组照人脸拼接检测

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492803

Divya S. Vidyadharan, S. Thampi

引用次数: 6

Inducing awareness for learners through visualizing mutual evaluation data by a self-organizing map 通过自组织图将相互评价数据可视化，引导学习者意识

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492795

Yuta Ueki, K. Ohnishi

引用次数: 1

Neural potential learning for tweets classification and interpretation 推文分类与解释的神经电位学习

2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2015-11-01 DOI: 10.1109/SOCPAR.2015.7492798

Ryozo Kitajima, R. Kamimura, O. Uchida, F. Toriumi

{"title":"Neural potential learning for tweets classification and interpretation","authors":"Ryozo Kitajima, R. Kamimura, O. Uchida, F. Toriumi","doi":"10.1109/SOCPAR.2015.7492798","DOIUrl":"https://doi.org/10.1109/SOCPAR.2015.7492798","url":null,"abstract":"The present paper aims to apply a new neural learning method called \"Neural Potential Learning, NPL\" to the classification and interpretation of tweets. It has been well known that social media such as the Twitter play crucial roles in transmitting important information at the time of natural disasters. In particular, since the Great East Japan Earthquake in 2011, the Twitter has been considered as one of the most efficient and convenient communication tools. However, because much redundant information is contained in the tweets, it is usually difficult to obtain important information from the flows of the tweets. Thus, it is urgently needed to develop some methods to extract the important and useful information from redundant tweets. To cope with complex and redundant data, a new neural potential learning has been developed to extract the important information. The method aims to find some highly potential neurons and enhance those neurons as much as possible to reduce redundant information and to focus on important information. The method was applied to the real tweets data collected in the earthquake and it was found that the method could classify the tweets as important and unimportant ones more accurately than the other conventional machine learning methods. In addition, the method made it possible to interpret how the tweets could be classified, based on the examination of highly potential neurons.","PeriodicalId":409493,"journal":{"name":"2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129349962","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: 4