TY -的A2 D使,乔凡尼盟——Song-men史PY - 2022 DA - 2022/01/13 TI -新疾病智能诊断方法基于模糊支持向量机增量学习SP - 7631271六世- 2022 AB -新疾病的诊断是一个具有挑战性的问题。早期的新疾病的出现,很少有样品;这可能会导致较低的智能诊断的准确性。因为优势的支持向量机(SVM)在处理小样本问题,是选择的智能诊断方法。标准支持向量机诊断模型更新需要重新培训所有样本。花费巨大的存储和计算成本,难以适应不断变化的现实。为了解决这个问题,本文提出了一种新的疾病诊断方法基于模糊支持向量机增量学习。根据支持向量机理论,支持向量集和边界样本集与支持向量机诊断模型提取。只有这些样本集被认为是在增量学习,确保准确性和减少计算和存储的成本。减少噪音的影响点造成的减少训练样本,FSVM用于更新诊断模型,泛化是改善。 The simulation results on the banana dataset show that the proposed method can improve the classification accuracy from 86.4% to 90.4%. Finally, the method is applied in COVID-19’s diagnostic. The diagnostic accuracy reaches 98.2% as the traditional SVM only gets 84%. With the increase of the number of case samples, the model is updated. When the training samples increase to 400, the number of samples participating in training is only 77; the amount of calculation of the updated model is small. SN - 1748-670X UR - https://doi.org/10.1155/2022/7631271 DO - 10.1155/2022/7631271 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -