2010 Ninth International Conference on Machine Learning and Applications最新文献_第6页

Determining Suitability of Locations for Installation of Solar Power Station Based on Probabilistic Inference 基于概率推理的太阳能电站选址适宜性确定

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.169

I. Colak, Ş. Sağiroğlu, M. Demirtaş, H. Kahraman

引用次数: 1

Energy Production and Economic Growth: A Causality Analaysis for Turkey Based on Computer 能源生产与经济增长:基于计算机的土耳其因果关系分析

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.103

O. Ozkan, M. Aktas, H. S. Kuyuk, S. Bayraktaroglu

引用次数: 1

Learning in Dynamic Environments: Application to the Identification of Hybrid Dynamic Systems 动态环境下的学习:在混合动态系统辨识中的应用

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.86

M. S. Mouchaweh

引用次数: 4

A New Approach to Classification with the Least Number of Features 一种特征数最少的分类新方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.28

Sascha Klement, T. Martinetz

引用次数: 3

An Optimal Regression Algorithm for Piecewise Functions Expressed as Object-Oriented Programs 面向对象程序中分段函数的最优回归算法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.149

Juan Luo, A. Brodsky

引用次数: 8

Learning to Be a Good Tour-Guide Robot 学习成为一个好的导游机器人

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.92

J. J. Rainer, R. Galán

{"title":"Learning to Be a Good Tour-Guide Robot","authors":"J. J. Rainer, R. Galán","doi":"10.1109/ICMLA.2010.92","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.92","url":null,"abstract":"Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create \"awareness\" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profile.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163361","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

Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks 利用进化的人工神经网络克服Alpha-Beta限制

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.125

Y. Gal, M. Avigal

引用次数: 0

Boosted Dynamic Cognitive Activity Recognition from Brain Images 增强动态认知活动识别的大脑图像

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.60

Jun Li, D. Tao

{"title":"Boosted Dynamic Cognitive Activity Recognition from Brain Images","authors":"Jun Li, D. Tao","doi":"10.1109/ICMLA.2010.60","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.60","url":null,"abstract":"Functional Magnetic Resonance Imaging (fMRI) has become an important diagnostic tool for measuring brain haemodynamics. Previous research on analysing fMRI data mainly focuses on detecting low-level neuron activation from the ensued haemodynamic activities. An important recent advance is to show that the high-level cognitive status is recognisable from a period of fMRI records. Nevertheless, it would also be helpful to reveal dynamics of cognitive activities during the period. In this paper, we tackle the problem of discovering the dynamic cognitive activities by proposing an algorithm of boosted structure learning. We employ statistic model of random fields to represent the dynamics of the brain. To exploit the rich fMRI observations with reasonable model complexity, we build multiple models, where one model links the cognitive activities to only a fraction of the fMRI observations. We combine the simple models by using an altered AdaBoost scheme for multi-class structure learning and show theoretical justification of the proposed scheme. Empirical test shows the method effectively links the physiological and the psychological activities of the brain.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128041334","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: 0

Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic 利用离群值和统计量的边际函数识别转录调控网络

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.48

Jinghua Gu, J. Xuan, Y. Wang, R. Riggins, R. Clarke

{"title":"Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic","authors":"Jinghua Gu, J. Xuan, Y. Wang, R. Riggins, R. Clarke","doi":"10.1109/ICMLA.2010.48","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.48","url":null,"abstract":"Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises in micro array data and false information in network connectivity data (e.g., ChIP-on-chip binding data, motif information, etc.), it still remains challenging to reconstruct gene regulatory networks for real biomedical applications such as human cancer studies. In this paper, we model the relationship between the genes that share the same transcription factors (TF) from the angle of regression. We propose a statistic called outlier sum testing the conditional significance of the target genes. A Gibbs strategy is utilized in order to estimate the marginal value of outlier sum from its conditional function. Based on the outlier sum statistic we are able to extract the true target genes that carry information about transcription factor activities (TFAs) from the whole population. As a proof-of-concept, we demonstrated the efficiency and robustness of the proposed method on both simulation data and yeast cell cycle data.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023699","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: 2

DC Bus Voltage Regulation of an Active Power Filter Using a Fuzzy Logic Controller 基于模糊控制器的有源电力滤波器直流母线电压调节

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.165

I. Colak, R. Bayindir, O. Kaplan, Ferhat Tas

引用次数: 19