{"title":"Selective decentralization to improve reinforcement learning in unknown linear noisy systems","authors":"Thanh Nguyen, S. Mukhopadhyay","doi":"10.1109/IESYS.2017.8233565","DOIUrl":"https://doi.org/10.1109/IESYS.2017.8233565","url":null,"abstract":"In this paper, we answer the question of to what extend selective decentralization could enhance the learning and control performance when the system is noisy and unknown. Compared to the previous works in selective decentralization, in this paper, we add the system noise as another complexity in the learning and control problem. Thus, we only perform analysis for some simple toy examples of noisy linear system. In linear system, the Halminton-Jaccobi-Bellman (HJB) equation becomes Riccati equation with closed-form solution. Our previous framework in learning and control unknown system is based on the following principle: approximating the system using identification in order to apply model-based solution. Therefore, this paper would explore the learning and control performance on two aspects: system identification error and system stabilization. Our results show that selective decentralization show better learning performance than the centralization when the noise level is low.","PeriodicalId":429982,"journal":{"name":"2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124044861","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}
{"title":"Automatic skin lesion analysis towards melanoma detection","authors":"Le Thu Thao, N. Quang","doi":"10.1109/IESYS.2017.8233570","DOIUrl":"https://doi.org/10.1109/IESYS.2017.8233570","url":null,"abstract":"Deep learning methods for image analysis have shown impressive performance in recent years. In this paper, we present deep learning based approaches to solve two problems in skin lesion analysis using a dermoscopic image containing skin tumor. In the first problem, we use a fully convolutional-deconvolutional architecture to automatically segment skin tumor from the surrounding skin. In the second problem, we use a simple convolutional neural network and VGG-16 architecture using transfer learning to address the two different tasks in skin tumor classification. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge, which consists of 2000 training samples and 600 testing samples. The result shows that the proposed methods achieve promising performances. In the first problem, the average value of Jaccard index for lesion segmentation using fully convolutional-deconvolutional architecture is 0.507. In the second problem, the values of area under the receiver operating characteristic curve (AUC) on two different lesion classifications using VGG16 with transfer learning are 0.763 and 0.869, respectively; the average value of AUC in two tasks is 0.816.","PeriodicalId":429982,"journal":{"name":"2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131590817","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}
{"title":"Reducing code bloat in Genetic Programming based on subtree substituting technique","authors":"Thi Huong Chu, Quang Uy Nguyen","doi":"10.1109/IESYS.2017.8233556","DOIUrl":"https://doi.org/10.1109/IESYS.2017.8233556","url":null,"abstract":"Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate Terminal (SAT-GP). The idea of SAT-GP is to select a portion of the largest individuals in each generation and then replace a random subtree in every individual in this portion by an approximate terminal of the similar semantics. SAT-GP is tested on twelve regression problems and its performance is compared to standard GP and the latest bloat control method (neat-GP). The experimental results are encouraging, SAT-GP achieved good performance on all tested problems regarding to the four popular performance metrics in GP research.","PeriodicalId":429982,"journal":{"name":"2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957859","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}