Semiovervåket

Semiovervåket, often translated as semi-supervised learning, is a machine learning approach that lies between unsupervised learning and supervised learning. It utilizes a dataset that contains a small amount of labeled data and a large amount of unlabeled data. The goal is to leverage the unlabeled data to improve the performance of a model that would otherwise be trained on only the limited labeled examples.

In supervised learning, a model learns from data where each input is paired with a correct output. Unsupervised learning, on the other hand, deals with unlabeled data, aiming to find patterns or structures within it, such as clustering. Semiovervåket learning bridges this gap by using the unlabeled data to infer properties of the data distribution, which can then aid in the learning process.

Common techniques in semiovervåket learning include self-training, where a model trained on labeled data is used to label the unlabeled data, and then retrained on the augmented dataset. Another approach is co-training, which uses multiple views of the data or multiple models to label unlabeled instances collaboratively. Generative models can also be employed to learn the underlying data distribution from both labeled and unlabeled data.

Semiovervåket learning is particularly useful in scenarios where obtaining large amounts of labeled data is expensive or time-consuming. Applications can be found in areas like image and speech recognition, natural language processing, and medical diagnosis, where unlabeled data is often abundant.