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Support subsets estimation for support vector machines retraining
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AI
DOI：10.1016/j.patcog.2022.109117.png)
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Abstract
En 中文
The availability of new data in previously trained Machine Learning ( ML ) models usually requires re-training and adjustment of the model. Support Vector Machines (SVMs) are widely used in ML because of their strong mathematical foundations and flexibility. However, SVM training is computationally ex-pensive, both in time and memory. Hence, the training phase might be a limitation in problems where the model is updated regularly. As a solution, new methods for training and updating SVMs have been proposed in the past. In this paper, we introduce the concept of Support Subset and a new retraining methodology for SVMs. A Support Subset is a subset of the training set, such that retraining a ML model with this subset and the new data is equivalent to training with all the data. The performance of the proposal is evaluated in a variety of experiments on simulated and real datasets in terms of time, quality of the solution, resultant support vectors, and amount of employed data. The promising results provide a new research line for improving the effectiveness and adaptability of the proposed technique, including its generalization to other ML models.(c) 2022 Published by Elsevier Ltd.
Keywords:
Support subset
SVM
Incremental learning
Retraining
Alpha seeding