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Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods

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Date
2018
Author
Çayır, Aykut
Yenidoğan, Işıl
Dağ, Hasan
Abstract
Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper, we propose a method, which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms, such as, support vector machine, gradient boosting machines, and random forest. All of the proposed hybrid models and the above mentioned machine learning algorithms are trained on three different datasets: MNIST, Fashion-MNIST, and CIFAR-10. Results show that the proposed hybrid models are more successful than traditional models while they are being trained from raw pixel values. In this study, we empower traditional machine learning algorithms for classification using feature extraction ability of deep neural network architectures and we are inspired by transfer learning methodology to this.

Source

UBMK 2018 - 3rd International Conference on Computer Science and Engineering

URI

https://doi.org/10.1109/UBMK.2018.8566383
https://hdl.handle.net/20.500.12469/3719

Collections

  • Araştırma Çıktıları / Scopus [1319]
  • Yönetim Bilişim Sistemleri / Management Information Systems [143]

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