semigecontroleerde

Semigecontroleerde is a Dutch term that translates to semi-supervised in English. It refers to a class of machine learning techniques that leverage a small amount of labeled data along with a large amount of unlabeled data for training. This approach is particularly useful when obtaining labeled data is expensive or time-consuming.

In semigecontroleerde learning, the model first learns from the labeled examples to understand the underlying patterns or relationships. Then, it uses this learned knowledge to infer labels for the unlabeled data. Various methods exist for this inference, such as self-training, where the model predicts labels for unlabeled data and then retrains itself with these newly labeled examples. Co-training is another approach where multiple models are trained on different views of the data, and they exchange their predictions to improve each other.

The effectiveness of semigecontroleerde learning often depends on the assumption that the unlabeled data is representative of the overall data distribution and that data points that are close to each other in the feature space are likely to share the same label. This method can significantly improve model performance compared to using only labeled data, especially when labeled data is scarce. It finds applications in areas like image classification, natural language processing, and speech recognition.