halvempiriskemetodebaserte
halvempiriskemetodebaserte is a term that refers to methods based on empirical risk minimization. In machine learning and statistics, empirical risk minimization is a fundamental principle used for model training. The goal is to find a model that minimizes the average error on a given dataset, which is considered a sample of the true underlying data distribution. This average error is often referred to as the empirical risk.
The methods that are halvempiriskemetodebaserte aim to find the best model parameters by directly optimizing this
While empirical risk minimization is a widely used and effective approach, it has limitations. A primary concern