2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)最新文献_第3页

Real detection intrusion using supervised and unsupervised learning 使用监督和无监督学习的真实检测入侵

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054151

Nouria Harbi, E. Bahri

引用次数: 6

Efficient tracking of industrial equipments using a wi-fi based localization system 使用基于wi-fi的定位系统高效跟踪工业设备

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054114

Nguyen Khac Vinh, Tran Quang Long, Nguyen Anh Viet, Duong Minh Tien, Vo Phuoc Hau, T. D. Souza-Daw, Trinh Dang, Ngoc Hoang Le, T. Hoang, Tien Dzung Nguyen

引用次数: 2

On the differential evolution for vehicle routing problem 车辆路径问题的差分演化

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054163

P. Krömer, A. Abraham, V. Snás̃el, E. Berhan, D. Kitaw

引用次数: 5

A more powerful random neural network model in supervised learning applications 一个更强大的随机神经网络模型在监督学习中的应用

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054127

Sebastián Basterrech, G. Rubino

引用次数: 4

Efficient implementation of a fractal color image compression on FPGA 分形彩色图像压缩在FPGA上的高效实现

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054124

T. Son, Tran V. Long, Hoang Manh Thang, Tien Dzung Nguyen

引用次数: 3

Named entity recognition in vietnamese text using label propagation 使用标签传播的越南文文本命名实体识别

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054160

H. T. Le, Rathany Chan Sam, Hoan Cong Nguyen, T. Nguyen

引用次数: 7

Paired feature constraints for latent dirichlet topic models 潜在dirichlet主题模型的配对特征约束

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054141

Bhattu Nagesh, Sristy, D. Somayajulu, R. Subramanyam, Phd Student

{"title":"Paired feature constraints for latent dirichlet topic models","authors":"Bhattu Nagesh, Sristy, D. Somayajulu, R. Subramanyam, Phd Student","doi":"10.1109/SOCPAR.2013.7054141","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054141","url":null,"abstract":"Non Parametric Bayes models, so called family of Latent Dirichlet Allocation (LDA) Topic Models have found application in various aspects of pattern recognition like sentiment analysis, information retrieval, question answering etc. The topics induced by LDA are used for later tasks such as classification, regression(movie ratings), ranking and recommendation. Recently various approaches are suggested to improve the utility of topics induced by LDA using various side-information such as labeled examples and labeled features. Pair-Wise feature constraints such as cannot-link and must-link, represent weak-supervision and are prevalent in domains such as sentiment analysis. Though must-link constraints are relatively easier to incorporate by using dirichlet tree, the cannot-link constraints are harder to incorporate using the dirichlet forest. In this paper we proposed an approach to address this problem using posterior constraints. We introduced additional latent variables for capturing the constraints, and modified the gibbs sampling algorithm to incorporate these constraints. Our method of Posterior Regularization has enabled us to deal with both types of constraints seamlessly in the same optimization framework. We have demonstrated our approach on a product sentiment review data set which is typically used in text analysis.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"498 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":"123063962","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

Higher order geometrical image features representation for action recognition 用于动作识别的高阶几何图像特征表示

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054140

N. N. A. Sjarif, S. Z. M. Hashim, S. Shamsuddin, A. Ralescu

引用次数: 0

Flock identification using connected components labeling for multi-robot shepherding 多机器人牧羊中使用连接组件标签的羊群识别

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054147

Sazalinsyah Razali, Nurul Fathiyah Shamsudin, Mashanum Osman, Q. Meng, Shuanghua Yang

{"title":"Flock identification using connected components labeling for multi-robot shepherding","authors":"Sazalinsyah Razali, Nurul Fathiyah Shamsudin, Mashanum Osman, Q. Meng, Shuanghua Yang","doi":"10.1109/SOCPAR.2013.7054147","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054147","url":null,"abstract":"Shepherding is often used in robotics and applied to various domains such as military in Unmanned Aerial Vehicle (UAV) or Unmanned Ground Vehicle (UGV) combat scenarios, disaster rescue and even in manufacturing. Generally, robot shepherding refers to a task of a robot known as shepherd or sheep herder, who guards and takes care of flocks of sheep, to make sure that the flock is intact and protect them from predators. In order to make an accurate decision, the shepherd needs to identify the flock that needs to be managed. How does the shepherd can precisely identify a group of animals as a flock? How can one actually judge a flock of sheep, is a flock? How does the shepherd decide how to approach or to steer the flock? These are the questions that relates to flock identification. In this paper, a new method using connected components labeling is proposed to cater the problem of flock identification in multi-robot shepherding scenarios. The results shows that it is a feasible approach, and can be used when integrated with the Player/Stage robotics simulation platform.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"1 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":"129589877","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

Clustering based neural network approach for classification of road images 基于聚类的神经网络道路图像分类方法

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054121

Tejy Kinattukara, B. Verma

引用次数: 9