Semi-Supervised Machine Learning

Semi-Supervised Machine Learning is a branch of Machine Learning that combines a small amount of labeled data with a large amount of unlabeled data during training. The goal is to improve learning accuracy by leveraging the structure and patterns present in the unlabeled data alongside the information provided by the labeled data.

In this approach, algorithms typically begin by learning from the labeled data, then use the unlabeled data to refine their understanding, often employing techniques like self-training, co-training, or graph-based methods.

Examples of semi-Supervised Machine Learning include:

• Text Classification: A model trained on a few labeled documents (e.g., spam vs. non-spam emails) and a larger set of unlabeled documents to improve Classification accuracy.
• Image Classification: Using a small number of labeled images (e.g., cat vs. dog) along with a large collection of unlabeled images to enhance the model’s ability to differentiate between categories.
• Speech Recognition: Combining a limited set of labeled audio clips with a vast amount of unlabeled recordings to boost the performance of speech recognition systems.

Use cases include:

• Medical Imaging: Training models with few labeled medical images (e.g., tumors) alongside numerous unlabeled scans to improve diagnostic accuracy.
• Web Content Classification: Classifying user-generated content by utilizing a small number of labeled examples (e.g., categories of posts) and a large amount of unlabeled data (e.g., user comments).
• Natural Language Processing: Enhancing language models by using a small set of annotated text (e.g., sentiment analysis) with a broader corpus of unannotated text.