Multi-Phase Training Techniques for Machine Learning Models Using Weighted Training Data

• Main Authors: Wang, Shuoyuan, Chen, Shi, Zhang, Jiaqi
• Format: Patent
• Language: English
• Published: 28.04.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

Techniques are disclosed relating to multi-phase training of machine learning models using weighted training data. In some embodiments, a computer system may train a machine learning classification model in at least two phases. During an initial training phase, the computer system may train an initial version of the classification model based on a training dataset, applying equal weight to the training samples in the training dataset. The computer system may then generate model scores for the training samples using the initial version of the classification model. Based on these model scores, the computer system may generate, for the training samples, corresponding weighting values. The computer system may then perform a subsequent training phase to generate an updated version of the classification model, where, during this subsequent training phase, at least some of the training samples are weighted using their respective weighting values.