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Step-by-step learning
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DOI：10.1016/j.eswa.2025.127939.png)
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Abstract
En 中文
The natural or generational learning process consists of building models based on available experiences. Each generation learns from the models obtained by its predecessors and obtains a new model for its own batch of experiences. In this paper, we discuss this step-by-step learning procedure for supervised classification and regression problems on large datasets. We show that the stepwise learning procedure performs competitively with respect to the approach that uses a single model for the entire dataset. This allows the step-by-step procedure to address larger datasets, and also, if necessary, respect the confidentiality of data from previous generations.
Keywords:
Machine learning
Batch processing
Federated learning
Regression
Supervised classification
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