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DeepRSD: A deep regression method for sequential data

Journal Article


Abstract


  • Regressions on Sequential Data (RSD) are widely used in different disciplines. This paper proposes DeepRSD, which utilizes several different neural networks to result in an effective end-to-end learning method for RSD problems. There have been several variants of deep Recurrent Neural Networks (RNNs) in classification problems. The main functional part of DeepRSD is the stacked bi-directional RNNs, which is the most suitable deep RNN model for sequential data. We explore several conditions to ensure a plausible training of DeepRSD. More importantly, we propose an alternative dropout to improve its generalization. We apply DeepRSD to two different real-world problems and achieve state-of-the-art performances. Through comparisons with state-of-the-art methods, we conclude that DeepRSD can be a competitive method for RSD problems.

Publication Date


  • 2018

Citation


  • Wang, X., Zhang, M. & Ren, F. (2018). DeepRSD: A deep regression method for sequential data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11012 LNAI 113-125.

Scopus Eid


  • 2-s2.0-85051932834

Number Of Pages


  • 12

Start Page


  • 113

End Page


  • 125

Volume


  • 11012 LNAI

Place Of Publication


  • Germany

Abstract


  • Regressions on Sequential Data (RSD) are widely used in different disciplines. This paper proposes DeepRSD, which utilizes several different neural networks to result in an effective end-to-end learning method for RSD problems. There have been several variants of deep Recurrent Neural Networks (RNNs) in classification problems. The main functional part of DeepRSD is the stacked bi-directional RNNs, which is the most suitable deep RNN model for sequential data. We explore several conditions to ensure a plausible training of DeepRSD. More importantly, we propose an alternative dropout to improve its generalization. We apply DeepRSD to two different real-world problems and achieve state-of-the-art performances. Through comparisons with state-of-the-art methods, we conclude that DeepRSD can be a competitive method for RSD problems.

Publication Date


  • 2018

Citation


  • Wang, X., Zhang, M. & Ren, F. (2018). DeepRSD: A deep regression method for sequential data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11012 LNAI 113-125.

Scopus Eid


  • 2-s2.0-85051932834

Number Of Pages


  • 12

Start Page


  • 113

End Page


  • 125

Volume


  • 11012 LNAI

Place Of Publication


  • Germany