Semiövervakat

Semiövervakat is a term used in machine learning to describe a type of supervised learning approach. It sits between fully supervised learning, where all training data is labeled, and unsupervised learning, where no data is labeled. In semiövervakat learning, a small amount of labeled data is combined with 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 only on the limited labeled data.

This technique is particularly useful in situations where obtaining labeled data is expensive or time-consuming. For example, in image recognition, labeling thousands of images manually can be a significant undertaking. With semiövervakat learning, a small set of labeled images can be used to guide the learning process on a much larger collection of unlabeled images. The unlabeled data can help the model learn the underlying structure or distribution of the data, which can then aid in better classification or regression.

Various algorithms fall under the umbrella of semiövervakat learning. Some common approaches include self-training, co-training, and generative models. Self-training involves training a model on labeled data, then using that model to predict labels for the unlabeled data, and finally retraining the model with both the original labeled data and the newly predicted labels. Co-training uses multiple classifiers trained on different views of the data. Generative models try to learn the joint probability distribution of data and labels. The effectiveness of semiövervakat learning depends on the assumption that the unlabeled data contains useful information about the structure of the data and the target function.