Twin support vector regression with Huber loss.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS(2017)
摘要
is an element of-insensitive loss function is often employed in the twin support vector regression (TSVR). However, it can not effectively address the data with Gaussian noise. Huber loss function can suppress a variety of noise and outliers and yields great generalization performance. Motivated by this, we propose a novel twin support vector regression with Huber loss for the noise data in this paper. Experiments on nine benchmark datasets with different Gaussian noise show the validity of our proposed algorithm. Finally, we apply our method to the financial time series data that usually contain noise and outlier, and it also produces great performance.
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关键词
Twin support vector regression,Gaussian loss,Huber loss,Gaussian noise
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