Semi-supervised learning is a machine learning approach that leverages both labeled and unlabeled data for training, aiming to improve learning accuracy compared to using only labeled data. It is particularly useful when acquiring a fully labeled dataset is expensive or time-consuming, allowing models to learn from a small amount of labeled data supplemented by a larger pool of unlabeled data.

Semi-supervised Learning

Semi-supervised learning

machine learning approach

Labeled data refers to datasets that have been tagged with one or more labels, which are used as ground truth for training machine learning models. It is crucial for supervised learning, enabling models to learn the relationship between input features and the desired output effectively.

labeled data

Unlabeled data refers to data that has not been annotated with labels or categories, making it challenging to use directly for supervised learning tasks. It is abundant and often used in unsupervised learning or semi-supervised learning to discover patterns or structure in the data without predefined labels.

Relevant Degrees

Your Lessons