Selected article for: "large scale and real world"
Author: Singla, Manisha; Ghosh, Debdas; Shukla, K. K.
Title: [Formula: see text] -TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients
  • Cord-id: ffw4gp2b
  • Document date: 2021_7_17
    • ID: ffw4gp2b
    Snippet: Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing labels. However, if these data sets are further corrupted with label noise, it becomes even more challenging to train a machine learning model on such data sets. We propose to use a transductive support vector machine (TSVM) for semi-supervised learning in this situation. We make this model robust to label noise by using a truncated pinball loss function
    Document: Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing labels. However, if these data sets are further corrupted with label noise, it becomes even more challenging to train a machine learning model on such data sets. We propose to use a transductive support vector machine (TSVM) for semi-supervised learning in this situation. We make this model robust to label noise by using a truncated pinball loss function with it. We name our approach, [Formula: see text] -TSVM. We provide both the primal and the dual formulations of the obtained robust TSVM for linear and non-linear kernels. We also perform experiments on synthetic and real-world data sets to prove the superior robustness of our model as compared to the existing approaches. To this end, we use small as well as large-scale data sets to perform the experiments. We show that the model is capable of training in the presence of label noise and finding the missing labels of the data samples. We use this property of [Formula: see text] -TSVM to detect the coronavirus patients based on their chest X-ray images.

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