2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)最新文献_第2页

Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence 基于计算智能的自学习数据处理框架，增强机器智能的自主控制

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009508

Prapa Rattadilok, Andrei V. Petrovski

引用次数: 1

On merging and dividing of Barabási-Albert-graphs 论Barabási-Albert-graphs的合并与分割

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009499

Pascal Held, Alexander Dockhorn, R. Kruse

引用次数: 2

Evolving maximum likelihood clustering algorithm 进化的最大似然聚类算法

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009511

Orlando Donato Rocha Filho, G. Serra

引用次数: 0

Topological stability of evolutionarily unstable strategies 进化不稳定策略的拓扑稳定性

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009501

D. Kasthurirathna, Piraveenan Mahendra

{"title":"Topological stability of evolutionarily unstable strategies","authors":"D. Kasthurirathna, Piraveenan Mahendra","doi":"10.1109/EALS.2014.7009501","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009501","url":null,"abstract":"Evolutionary game theory is used to model the evolution of competing strategies in a population of players. Evolutionary stability of a strategy is a dynamic equilibrium, in which any competing mutated strategy would be wiped out from a population. If a strategy is weak evolutionarily stable, the competing strategy may manage to survive within the network. Understanding the network-related factors that affect the evolutionary stability of a strategy would be critical in making accurate predictions about the behaviour of a strategy in a real-world strategic decision making environment. In this work, we evaluate the effect of network topology on the evolutionary stability of a strategy. We focus on two well-known strategies known as the Zero-determinant strategy and the Pavlov strategy. Zero-determinant strategies have been shown to be evolutionarily unstable in a well-mixed population of players. We identify that the Zero-determinant strategy may survive, and may even dominate in a population of players connected through a non-homogeneous network. We introduce the concept of `topological stability' to denote this phenomenon. We argue that not only the network topology, but also the evolutionary process applied and the initial distribution of strategies are critical in determining the evolutionary stability of strategies. Further, we observe that topological stability could affect other well-known strategies as well, such as the general cooperator strategy and the cooperator strategy. Our observations suggest that the variation of evolutionary stability due to topological stability of strategies may be more prevalent in the social context of strategic evolution, in comparison to the biological context.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132997253","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

An ensemble method based on evolving classifiers: eStacking 基于进化分类器的集成方法:层叠

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009513

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

引用次数: 8

A real-time approach for autonomous detection and tracking of moving objects from UAV 一种无人机运动目标自动检测与跟踪的实时方法

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-12-01 DOI: 10.1109/EALS.2014.7009502

Pouria Sadeghi-Tehran, Christopher Clarke, P. Angelov

引用次数: 13

Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model 基于神经网络模型的fMRI脑时空数据分类与分割

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-01-13 DOI: 10.1109/EALS.2014.7009506

M. Doborjeh, E. Capecci, N. Kasabov

{"title":"Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube evolving Spiking Neural Network model","authors":"M. Doborjeh, E. Capecci, N. Kasabov","doi":"10.1109/EALS.2014.7009506","DOIUrl":"https://doi.org/10.1109/EALS.2014.7009506","url":null,"abstract":"The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period [1]; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube [2]. After the NeuCube is trained with the fMRI samples, the `hidden' spatio- temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results [3] on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.","PeriodicalId":216554,"journal":{"name":"2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128871328","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: 28

A recurrent meta-cognitive-based Scaffolding classifier from data streams 基于元认知的数据流循环脚手架分类器

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) Pub Date : 2014-01-13 DOI: 10.1109/EALS.2014.7009514

Mahardhika Pratama, Jie Lu, S. Anavatti, J. A. Iglesias

引用次数: 0