2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

Predicting Future Agent Motions for Dynamic Environments 预测未来动态环境中的智能体运动

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0024

Fabio Previtali, Alejandro Bordallo, L. Iocchi, S. Ramamoorthy

{"title":"Predicting Future Agent Motions for Dynamic Environments","authors":"Fabio Previtali, Alejandro Bordallo, L. Iocchi, S. Ramamoorthy","doi":"10.1109/ICMLA.2016.0024","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0024","url":null,"abstract":"Understanding activities of people in a monitored environment is a topic of active research, motivated by applications requiring context-awareness. Inferring future agent motion is useful not only for improving tracking accuracy, but also for planning in an interactive motion task. Despite rapid advances in the area of activity forecasting, many state-of-the-art methods are still cumbersome for use in realistic robots. This is due to the requirement of having good semantic scene and map labelling, as well as assumptions made regarding possible goals and types of motion. Many emerging applications require robots with modest sensory and computational ability to robustly perform such activity forecasting in high density and dynamic environments. We address this by combining a novel multi-camera tracking method, efficient multi-resolution representations of state and a standard Inverse Reinforcement Learning (IRL) technique, to demonstrate performance that is better than the state-of-the-art in the literature. In this framework, the IRL method uses agent trajectories from a distributed tracker and estimates a reward function within a Markov Decision Process (MDP) model. This reward function can then be used to estimate the agent's motion in future novel task instances. We present empirical experiments using data gathered in our own lab and external corpora (VIRAT), based on which we find that our algorithm is not only efficiently implementable on a resource constrained platform but is also competitive in terms of accuracy with state-of-the-art alternatives (e.g., up to 20% better than the results reported in [1]).","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122214707","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

Learning the Domain of Sparse Matrices 学习稀疏矩阵的域

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0143

Suleyman Salin, M. Manguoglu, H. Aktulga

引用次数: 1

Fast Nearest Neighbor Search with Transformed Residual Quantization 基于变换残差量化的快速近邻搜索

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0175

Jiangbo Yuan, Xiuwen Liu

引用次数: 2

Sentiment Analysis of Restaurant Reviews on Yelp with Incremental Learning 基于增量学习的Yelp餐厅评论情感分析

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0123

Tri Doan, J. Kalita

引用次数: 17

Validation of a Federation of Collaborative Rational Agents for the Diagnosis of Acute Coronary Syndromes in a Population with High Probability 在高概率人群中诊断急性冠脉综合征的协作理性代理联盟的验证

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0125

J. Sprockel, J. Diaztagle, Alberto Llanos, Cristian Castillo, Enríque González Guerrero

{"title":"Validation of a Federation of Collaborative Rational Agents for the Diagnosis of Acute Coronary Syndromes in a Population with High Probability","authors":"J. Sprockel, J. Diaztagle, Alberto Llanos, Cristian Castillo, Enríque González Guerrero","doi":"10.1109/ICMLA.2016.0125","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0125","url":null,"abstract":"Acute myocardial infarction is the main cause of death worldwide, it is part of the acute coronary syndromes (ACS) which are characterized by an acute obstruction of the blood flow in the arteries of the heart. ACS diagnosis poses a highly complex problem where the use of intelligent systems represents an opportunity for the optimization of the diagnosis. The objective of the present work is to perform a cross validation of a federation of collaborative rational agents for the diagnosis of ACS in a population with high probability exhibiting chest pain. A study of diagnostic tests was performed, the diagnostic standard criterion was the third redefinition of infarction or some strategy for coronary stratification. The index test was the result of a system based on a federation of collaborative rational agents based on the assembly of neural networks by means of a weighted voting system in accordance with positive likelihood ratios. A sample of 108 patients was calculated and a contingency table was built in order to calculate the operational characteristics. 148 patients were taken into consideration, ACS was discarded in 29,2%, 51,7 exhibited acute infarction, and 19,1% exhibited unstable angina. The federation system reached a precision of 79%, sensibility of 97,1%, specificity of 36,4%, and AUC of 0,672. It is concluded that a multi-agent system based on the assembly of neural networks attained an acceptable performance for the diagnosis of ACS in a population with high probability.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134504129","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

Analysis for Status of the Road Accident Occurance and Determination of the Risk of Accident by Machine Learning in Istanbul 伊斯坦布尔道路交通事故发生现状分析及事故风险的机器学习判定

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0075

H. Bulbul, T. Kaya, Yusuf Tulgar

引用次数: 16

Real-Time Activity Classification by Matched Filtering Using Body-Worn Accelerometers 基于穿戴式加速度计匹配滤波的实时活动分类

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0192

C. Euler, C. T. Lin, Bryan Juarez, Melissa Flores

引用次数: 0

User Movement Prediction: The Contribution of Machine Learning Techniques 用户运动预测:机器学习技术的贡献

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0100

Shadi Banitaan, Mohammad Azzeh, A. B. Nassif

引用次数: 13

ADHD and ASD Classification Based on Emotion Recognition Data 基于情绪识别数据的ADHD和ASD分类

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0145

Mahiye Uluyagmur-Ozturk, A. Arman, Seval Sultan Yilmaz, O. P. Findik, H. A. Genç, Gresa Carkaxhiu-Bulut, M. Yazgan, Umut Teker, Z. Cataltepe

{"title":"ADHD and ASD Classification Based on Emotion Recognition Data","authors":"Mahiye Uluyagmur-Ozturk, A. Arman, Seval Sultan Yilmaz, O. P. Findik, H. A. Genç, Gresa Carkaxhiu-Bulut, M. Yazgan, Umut Teker, Z. Cataltepe","doi":"10.1109/ICMLA.2016.0145","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0145","url":null,"abstract":"In this work, we focused on classification of the participants with Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and typically developing children, based on their performances during an emotion recognition experiment that we developed. We prepared an experiment environment where participants were shown images of faces of people exhibiting certain emotions up to a certain strength and then they answered the question \"What is the emotion of this person?\". The response and response latency of the participants were recorded and used for the classification process. Before the classification step, in order to select the relevant images which are used as features in this work, ReliefF feature selection algorithm was used. Machine learning feature selection and classification algorithms were used on different definitions of the classification problem where the differentiation between two classes against each other or one class against the other two classes were aimed. The selected features (images shown) and the classification performance changed based on the classification problem definition.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128546797","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: 10

Interpretation Method of Nonlinear Multilayer Principal Component Analysis by Using Sparsity and Hierarchical Clustering 非线性多层主成分分析的稀疏性和层次聚类解释方法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0193

N. Koda, Sumio Watanabe

引用次数: 0