Analysis and Review of Extraordinary Machine Learning Approaches

  • Authors

    • Nitin Tyagi
    • Sandeep Gupta
    • Arun Pratap Srivastava
    • Shashank Awasthi
    • . .
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.27728
  • Random Recursive Support Vector Machine (R2SVM), Stacked Sparse autoencoder (SSAE), Stacked Denoising Autoencoder (SDAE), Convolutional Neural Network (CNN), Support Vector Machine (SVM)

  • Abstract

    Machine learning is utilized in the medicinal imaging field, including PC supported finding, picture division, picture enrollment, picture combination, picture guided treatment, picture explanation, furthermore, picture database recovery. Deep learning techniques are arrangements of calculations in machine learning, which endeavor to consequently take in numerous levels of portrayal and deliberation that assistance, comprehend information. This thusly prompts the need of comprehension and looking at the attributes of profound learning approaches, with the end goal to have the capacity to prosecute and process the strategies in legitimate manner. The aim of this article is to assess deep learning (DL) strategies for therapeutic area also to comprehend if DL techniques (random recursive support vector machines (R2SVM), stacked sparse auto-encoders (SSAE), stacked denoising auto-encoders (SDAE), K-means deep learning calculation) beat other best in class approaches (K-closest neighbor, support vector machines, greatly randomized trees) on two arrangement undertakings, where different strategies are assessed for a transcribed digit (MNIST). Results demonstrate that the SSAE, SDAE what's more, the SVM accomplish the most noteworthy exactness among all assessed methodologies on both datasets.

     

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  • How to Cite

    Tyagi, N., Gupta, S., Pratap Srivastava, A., Awasthi, S., & ., . (2018). Analysis and Review of Extraordinary Machine Learning Approaches. International Journal of Engineering & Technology, 7(4.39), 915-920. https://doi.org/10.14419/ijet.v7i4.39.27728

    Received date: 2019-02-21

    Accepted date: 2019-02-21

    Published date: 2018-12-13