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Weston, J. (n.d.) Support Vector Machine (and Statistical Learning Theory) Tutorial. NEC Labs America. http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf

has been cited by the following article:

  • TITLE: Machine Learning Approaches to Predict Default of Credit Card Clients

    AUTHORS: Ruilin Liu

    KEYWORDS: Machine Learning, Feedforward Neural Network, Long Short-Term Memory, Dropout

    JOURNAL NAME: Modern Economy, Vol.9 No.11, November 19, 2018

    ABSTRACT: This paper compares traditional machine learning models, i.e. Support Vector Machine, k-Nearest Neighbors, Decision Tree and Random Forest, with Feedforward Neural Network and Long Short-Term Memory. We observe that the two neural networks achieve higher accuracies than traditional models. This paper also tries to figure out whether dropout can improve accuracy of neural networks. We observe that for Feedforward Neural Network, applying dropout can lead to better performances in certain cases but worse performances in others. The influence of dropout on LSTM models is small. Therefore, using dropout does not guarantee higher accuracy.