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دسته بندی:
تشخیص الگو - Pattern recognition
سال انتشار:
2019
عنوان انگلیسی مقاله:
Multi-task least squares twin support vector machine for classification
ترجمه فارسی عنوان مقاله:
حداقل مربعات جزئی چند وظیفه ای ماشین بردار پشتیبانی برای طبقه بندی
منبع:
Sciencedirect - Elsevier - Neurocomputing, 338 (2019) 26-33: doi:10:1016/j:neucom:2018:12:079
نویسنده:
Benshan Mei a , Yitian Xu b ,
چکیده انگلیسی:
With the bloom of machine learning, pattern recognition plays an important role in many aspects. How- ever, traditional pattern recognition mainly focuses on single task learning (STL), and the multi-task learning (MTL) has largely been ignored. Compared to STL, MTL can improve the performance of learn- ing methods through the shared information among all tasks. Inspired by the recently proposed di- rected multi-task twin support vector machine (DMTSVM) and the least squares twin support vector ma- chine (LSTWSVM), we put forward a novel multi-task least squares twin support vector machine (MTLS- TWSVM). Instead of two dual quadratic programming problems (QPPs) solved in DMTSVM, our algorithm only needs to deal with two smaller linear equations. This leads to simple solutions, and the calculation can be effectively accelerated. Thus, our proposed model can be applied to the large scale datasets. In addition, it can deal with linear inseparable samples by using kernel trick. Experiments on three popular multi-task datasets show the effectiveness of our proposed methods. Finally, we apply it to two popular image datasets, and the experimental results also demonstrate the validity of our proposed algorithm.
Keywords: Pattern recognition | Multi-task learning | Relation learning | Least square twin support vector machine
قیمت: رایگان
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