2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)最新文献

Using a map-based encoding to evolve plastic neural networks 使用基于地图的编码来进化塑性神经网络

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945909

Paul Tonelli, Jean-Baptiste Mouret

引用次数: 11

Content-based audio classification using collective network of binary classifiers 基于内容的音频分类使用二元分类器的集体网络

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945911

Toni Mäkinen, S. Kiranyaz, M. Gabbouj

{"title":"Content-based audio classification using collective network of binary classifiers","authors":"Toni Mäkinen, S. Kiranyaz, M. Gabbouj","doi":"10.1109/EAIS.2011.5945911","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945911","url":null,"abstract":"In this paper, a novel collective network of binary classifiers (CNBC) framework is presented for content-based audio classification. The topic has been studied in several publications before, but in many cases the number of different classification categories is quite limited and needed to be fixed a priori. We focus our efforts to increase both the classification accuracy and the number of classes, as well as to create a scalable network design, which allows introducing new audio classes incrementally. The approach is based on dividing a major classification problem into several networks of binary classifiers (NBCs), where each NBC adapts its internal topology according to the classification problem at hand, by using evolutionary Artificial Neural Networks (ANNs). In the current work, feed-forward ANNs, or the so-called Multilayer Perceptrons (MLPs), are evolved within an architecture space, where a stochastic optimization is applied to seek for the optimal classifier configuration and parameters. The performance evaluations of the proposed framework over an 8-class benchmark audio database demonstrate its scalability and notable potential, as classification error rates of less than 9% are achieved.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128384916","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

A genetic algorithm for self-optimization in safety-critical resource-flow systems 安全关键型资源流系统自优化的遗传算法

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945915

Florian Siefert, Florian Nafz, H. Seebach, W. Reif

引用次数: 3

Kernel evolution for support vector classification 支持向量分类的核演化

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945924

Mehrdad Alizadeh, M. Ebadzadeh

引用次数: 3

Self-growing applications from abstract architectures an application to data-mediation systems 从抽象体系结构到数据中介系统的自增长应用程序

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945907

A. Diaconescu, P. Lalanda

{"title":"Self-growing applications from abstract architectures an application to data-mediation systems","authors":"A. Diaconescu, P. Lalanda","doi":"10.1109/EAIS.2011.5945907","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945907","url":null,"abstract":"Imagine a distributed mediation application consisting of hundreds of thousands of interconnected nodes, collecting data from millions of pervasive sensors, processing data and delivering it to a myriad of business services. This application takes the form of an acyclic, directed graph. Its shape must continually adapt in response to changes in sensor availability, network layout and business objectives. This involves dynamically adding, configuring, migrating and removing graph nodes. A centralised Observer/Controller, or Autonomic Manager (AM), that controls lifecycle operations for the entire graph would neither scale with the system's size and adaptation frequency, nor survive in unpredictable environments. This paper proposes a decentralised solution for enabling mediation applications to self-grow and to self-adapt their shapes and behaviours. In this approach, applications can autonomously grow into instances of a predefined, abstract architectural model and continually adapt to their execution conditions. A proof-of-concept prototype was developed using a Java-based, Service Oriented Component technology - iPOJO / OSGi. Experimental results from a Home Monitoring data-mediation scenario show the applicability and viability of our approach. We believe that the proposed framework will enable applications to autonomously grow and survive in volatile execution environments, over extended time periods.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"544 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133691996","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

Evolution of an adaptive unsupervised speech controlled system 自适应无监督语音控制系统的演化

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945906

T. Herbig, F. Gerl, W. Minker

引用次数: 3

A Simplified Structure Evolving Method for Fuzzy System structure learning 一种用于模糊系统结构学习的简化结构演化方法

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945914

Di Wang, Xiao-Jun Zeng, J. Keane

引用次数: 1

Kernel density estimation with stream data based on self-organizing map 基于自组织映射的流数据核密度估计

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945929

Haibo He, Yuan Cao

引用次数: 0

Evolving human activity classifier from sensor streams 从传感器流进化人类活动分类器

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945921

J. A. Iglesias, P. Angelov, Agapito Ledezma, A. Sanchis

引用次数: 0

Collective network of evolutionary binary classifiers for content-based image retrieval 基于内容的图像检索的进化二元分类器集体网络

2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2011-04-11 DOI: 10.1109/EAIS.2011.5945925

S. Kiranyaz, Stefan Uhlmann, Jenni Raitoharju, M. Gabbouj, T. Ince

{"title":"Collective network of evolutionary binary classifiers for content-based image retrieval","authors":"S. Kiranyaz, Stefan Uhlmann, Jenni Raitoharju, M. Gabbouj, T. Ince","doi":"10.1109/EAIS.2011.5945925","DOIUrl":"https://doi.org/10.1109/EAIS.2011.5945925","url":null,"abstract":"The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a “Divide and Conquer” type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability; and a significant performance improvement is achieved over traditional retrieval techniques.","PeriodicalId":243348,"journal":{"name":"2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121702394","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