کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
494604 862801 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust least squares twin support vector machine for human activity recognition
ترجمه فارسی عنوان
کمترین مربعات دوقلو با پشتیبانی از دستگاه بردار برای شناسایی فعالیت های انسانی
کلمات کلیدی
ماشین بردار حامی دوقلو، کمترین مربعات دوتایی دستگاه بردار پشتیبانی، طبقه بندی چند طبقه بندی ساختار درختی دودویی، درخت تصمیم گیری سه گانه، به رسمیت شناختن فعالیت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• Introduces hierarchical approach to deal with multi-class activity classification problem.
• LS-TWSVM based classifier that deals with noise in activity recognition framework.
• Introduce the incremental version of RLS-TWSVM in activity recognition framework.

Human activity recognition is an active area of research in Computer Vision. One of the challenges of activity recognition system is the presence of noise between related activity classes along with high training and testing time complexity of the system. In this paper, we address these problems by introducing a Robust Least Squares Twin Support Vector Machine (RLS-TWSVM) algorithm. RLS-TWSVM handles the heteroscedastic noise and outliers present in activity recognition framework. Incremental RLS-TWSVM is proposed to speed up the training phase. Further, we introduce the hierarchical approach with RLS-TWSVM to deal with multi-category activity recognition problem. Computational comparisons of our proposed approach on four well-known activity recognition datasets along with real world machine learning benchmark datasets have been carried out. Experimental results show that our method is not only fast but, yields significantly better generalization performance and is robust in order to handle heteroscedastic noise and outliers.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 47, October 2016, Pages 33–46
نویسندگان
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