2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第3页

Student Retention Pattern Prediction Employing Linguistic Features Extracted from Admission Application Essays 从入学申请论文中提取语言特征的学生保留模式预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-106

M. Ogihara, Gang Ren

引用次数: 3

Deep Mixture of Experts with Diverse Task Spaces 具有不同任务空间的深度混合专家

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-74

Jianping Fan, Tianyi Zhao, Zhenzhong Kuang, Zhou Yu, Jun Yu

引用次数: 2

Subject-Dependent SSVEP Identification Using GMM Training and Adaptation 基于GMM训练和适应的受试者依赖SSVEP识别

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-131

O. Dehzangi, Muhamed Farooq

{"title":"Subject-Dependent SSVEP Identification Using GMM Training and Adaptation","authors":"O. Dehzangi, Muhamed Farooq","doi":"10.1109/ICMLA.2017.0-131","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-131","url":null,"abstract":"The use of Brain Computer Interface (BCI) systems in the Intensive Care Unit (ICU) can facilitate communication on demand. BCI systems enable ICU patients to communicate using the electrical activity of their brains. For this purpose, we designed and developed a BCI system comprised of an Android tablet that allows patients to look at the screen to ask for what they need using their Electroencephalogram (EEG) recorded using a wireless wearable BCI. However, there are two main challenges associated with the BCI application. Due to the insufficient screen refresh rate of the mobile device, the flickering stimuli is imprecise. Hence, we introduce a partition-based feature extraction and fusion method using Canonical Correlation Analysis (CCA) and Power Spectral Density Analysis (PSDA) to overcome this limitation. Also, BCI devices require a calibration stage in order to capture subject-specific information, which might be particularly troublesome for ICU patients. WE hypothesize that inducing subject related information in the model training and adaptation improves the overall SSVEP identification performance with minimal calibration requirements. As such, We propose a three stage Gaussian Mixture Model (GMM)-based model training and subject adaptation: 1) we generate a subject independent universal GMM model, 2) we generate subject-dependent identification models using only a few collected SSVEP segments from each patient, and 3) we form a vector out of the subject-dependent GMMs and pass it to Support Vector Machine (SVM) for SSVEP target frequency identification. Our experimental results on 10 subjects demonstrated that the proposed framework yielded very efficient SSVEP identification performances achieving 98.7% accuracy using our most accurate model.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"86 1","pages":"384-389"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84795154","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: 1

Automatic Fault Diagnosis of Drills Using Artificial Neural Networks 基于人工神经网络的钻机故障自动诊断

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-23

Caleb Vununu, Ki-Ryong Kwon, Eung-Joo Lee, Kwang-Seok Moon, Suk-Hwan Lee

引用次数: 7

Privacy Setting Recommendation for Image Sharing 图片共享的隐私设置建议

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-73

Jun Yu, Zhenzhong Kuang, Zhou Yu, D. Lin, Jianping Fan

引用次数: 9

Semi-Automated Segmentation of Glioblastomas in Brain MRI Using Machine Learning Techniques 利用机器学习技术对脑MRI中胶质母细胞瘤进行半自动分割

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00017

Naomi Joseph, Parita Sanghani, Hongliang Ren

引用次数: 4

Railway Incident Ranking with Machine Learning 基于机器学习的铁路事故排序

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-95

Evgeni Bikov, P. Boyko, Evgeny Sokolov, D. Yarotsky

引用次数: 4

Geometrical Analysis of Machine Learning Security in Biometric Authentication Systems 生物识别认证系统中机器学习安全性的几何分析

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-142

Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, S. Gupta

引用次数: 16

Mixed Type Multi-attribute Pairwise Comparisons Learning 混合型多属性两两比较学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.000-2

N. N. Qomariyah, D. Kazakov

引用次数: 0

Multiple Kernel Representation Learning for WiFi-Based Human Activity Recognition 基于wifi的人体活动识别的多核表示学习

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-148

Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, C. Spanos

{"title":"Multiple Kernel Representation Learning for WiFi-Based Human Activity Recognition","authors":"Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, C. Spanos","doi":"10.1109/ICMLA.2017.0-148","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-148","url":null,"abstract":"Human activity recognition is becoming the vital underpinning for a myriad of emerging applications in the field of human-computer interaction, mobile computing, and smart grid. Besides the utilization of up-to-date sensing techniques, modern activity recognition systems also require a machine learning (ML) algorithm that leverages the sensory data for identification purposes. In view of the unique characteristics of the measurement data and the ML challenges thereof, we propose a non-intrusive human activity recognition system that only uses existing commodity WiFi routers. The core of our system is a novel multiple kernel representation learning (MKRL) framework that automatically extracts and combines informative patterns from the Channel State Information (CSI) measurements. The MKRL firstly learns a kernel string representation from time, frequency, wavelet, and shape domains with an efficient greedy algorithm. Then it performs information fusion from diverse perspectives based on multi-view kernel learning. Moreover, different stages of MKRL can be seamlessly integrated into a multiple kernel learning framework to build up a robust and comprehensive activity classifier. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that the proposed system outperforms existing methods and achieves a 98% activity recognition accuracy.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"422 1","pages":"268-274"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84932830","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: 17