2012 11th International Conference on Machine Learning and Applications最新文献_第6页

Obtaining Full Regularization Paths for Robust Sparse Coding with Applications to Face Recognition 鲁棒稀疏编码的全正则化路径及其在人脸识别中的应用

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.66

J. Chorowski, J. Zurada

引用次数: 2

Taxonomic Dimensionality Reduction in Bayesian Text Classification 贝叶斯文本分类中的分类降维

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.93

Richard A. McAllister, John W. Sheppard

引用次数: 0

Face Recognition in the Virtual World: Recognizing Avatar Faces 虚拟世界中的人脸识别:识别化身的面孔

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.16

Roman V. Yampolskiy, Brendan Klare, Anil K. Jain

{"title":"Face Recognition in the Virtual World: Recognizing Avatar Faces","authors":"Roman V. Yampolskiy, Brendan Klare, Anil K. Jain","doi":"10.1109/ICMLA.2012.16","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.16","url":null,"abstract":"Criminal activity in virtual worlds is becoming a major problem for law enforcement agencies. Forensic investigators are becoming interested in being able to accurately and automatically track people in virtual communities. In this paper a set of algorithms capable of verification and recognition of avatar faces with high degree of accuracy are described. Results of experiments aimed at within-virtual-world avatar authentication and inter-reality-based scenarios of tracking a person between real and virtual worlds are reported. In the FERET-to-Avatar face dataset, where an avatar face was generated from every photo in the FERET database, a COTS FR algorithm achieved a near perfect 99.58% accuracy on 725 subjects. On a dataset of avatars from Second Life, the proposed avatar-to-avatar matching algorithm (which uses a fusion of local structural and appearance descriptors) achieved average true accept rates of (i) 96.33% using manual eye detection, and (ii) 86.5% in a fully automated mode at a false accept rate of 1.0%. A combination of the proposed face matcher and a state-of-the art commercial matcher (FaceVACS) resulted in further improvement on the inter-reality-based scenario.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115485413","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: 38

Semantic Data Types in Machine Learning from Healthcare Data 医疗保健数据机器学习中的语义数据类型

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.41

Janusz Wojtusiak

引用次数: 10

Feature Mapping and Fusion for Music Genre Classification 音乐类型分类的特征映射与融合

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.59

H. Balti, H. Frigui

引用次数: 3

Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets 从推特的使用和推特的语言分析预测黑暗人格特质

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.218

Chris Sumner, A. Byers, Rachel Boochever, Gregory J. Park

{"title":"Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets","authors":"Chris Sumner, A. Byers, Rachel Boochever, Gregory J. Park","doi":"10.1109/ICMLA.2012.218","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.218","url":null,"abstract":"Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively unexplored is that of the anti-social traits of narcissism, Machiavellians and psychopathy, commonly referred to as the Dark Triad. This study explored the extent to which it is possible to determine anti-social personality traits based on Twitter use. This was performed by comparing the Dark Triad and Big Five personality traits of 2,927 Twitter users with their profile attributes and use of language. Analysis shows that there are some statistically significant relationships between these variables. Through the use of crowd sourced machine learning algorithms, we show that machine learning provides useful prediction rates, but is imperfect in predicting an individual's Dark Triad traits from Twitter activity. While predictive models may be unsuitable for predicting an individual's personality, they may still be of practical importance when models are applied to large groups of people, such as gaining the ability to see whether anti-social traits are increasing or decreasing over a population. Our results raise important questions related to the unregulated use of social media analysis for screening purposes. It is important that the practical and ethical implications of drawing conclusions about personal information embedded in social media sites are better understood.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126209517","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: 266

Measuring the Spatial Error in Load Forecasting for Electrical Distribution Planning as a Problem of Transporting the Surplus to the In-Deficit Locations 基于余量输缺问题的配电规划负荷预测空间误差测量

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.203

D. Vieira, M. A. M. Cabral, T. V. Menezes, B. E. Silva, A. C. Lisboa

引用次数: 8

A Treeboost Model for Software Effort Estimation Based on Use Case Points 基于用例点的软件工作量评估Treeboost模型

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.155

A. B. Nassif, Luiz Fernando Capretz, D. Ho, Mohammad Azzeh

引用次数: 55

Web Spam: A Study of the Page Language Effect on the Spam Detection Features 网页垃圾邮件:网页语言对垃圾邮件检测特征的影响研究

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.229

A. Alarifi, Mansour Alsaleh

引用次数: 14

Risk Estimation in Spatial Disease Clusters: An RBF Network Approach 基于RBF网络的空间疾病集群风险评估

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.233

Fernanda C. Takahashi, Ricardo H. C. Takahashi

{"title":"Risk Estimation in Spatial Disease Clusters: An RBF Network Approach","authors":"Fernanda C. Takahashi, Ricardo H. C. Takahashi","doi":"10.1109/ICMLA.2012.233","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.233","url":null,"abstract":"This paper proposes a method which is suitable for the estimation of the probability of occurrence of a syndrome, as a function of the geographical coordinates of the individuals under risk. The data describing the location of syndrome cases over the population suffers a moving-average filtering, and the resulting values are fitted by an RBF network performing a regression. Some contour curves of the RBF network are then employed in order to establish the boundaries between four kinds of regions: regions of high-incidence, regions of medium incidence, regions of slightly-abnormal incidence, and regions of normal prevalence. In each region, the risk is estimated with three indicators: a nominal risk, an upper bound risk and a lower bound risk. Those indicators are obtained by adjusting the probability employed for the Monte Carlo simulation of syndrome scenarios over the population. The nominal risk is the probability which produces Monte Carlo simulations for which the empirical number of syndrome cases corresponds to the median. The upper bound and the lower bound risks are the probabilities which produce Monte Carlo simulations for which the empirical values of syndrome cases correspond respectively to the 25% percentile and the 75% percentile. The proposed method constitutes an advance in relation to the currently known techniques of spatial cluster detection, which are dedicated to finding clusters of abnormal occurrence of a syndrome, without quantifying the probability associated to such an abnormality, and without performing a stratification of different sub-regions with different associated risks. The proposed method was applied on data which were studied formerly in a paper that was intended to find a cluster of dengue fever. The result determined here is compatible with the cluster that was found in that reference.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122027312","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