Semikontrollitud

Semikontrollitud refers to a type of machine learning where the training dataset contains a small amount of labeled data and a large amount of unlabeled data. This contrasts with supervised learning, which uses entirely labeled data, and unsupervised learning, which uses entirely unlabeled data. The goal of semikontrollitud learning is to leverage the abundant unlabeled data to improve the performance of a model that would otherwise be trained on limited labeled examples.

The underlying principle is that the unlabeled data, even without explicit labels, can still provide valuable information about the structure of the data distribution. Algorithms in this field attempt to infer relationships, clusters, or underlying patterns from the unlabeled examples, which then help to refine the decision boundaries or representations learned from the labeled data. This can lead to more robust and accurate models, especially in scenarios where acquiring labeled data is expensive or time-consuming.

Common techniques in semikontrollitud learning include self-training, co-training, and graph-based methods. Self-training involves training a model on labeled data, then using that model to predict labels for unlabeled data, and finally retraining the model on the combined set of original labeled data and newly pseudo-labeled data. Co-training uses multiple views of the data, with separate models trained on each view and then used to label data for each other. Graph-based methods construct a graph where nodes represent data points and edges represent similarity, then propagate labels from labeled nodes to unlabeled nodes. Semikontrollitud learning is particularly useful in domains like image classification, natural language processing, and fraud detection where large amounts of unlabeled data are readily available.