What Semi-supervised Learning Meaning, Applications & Example
A learning approach that combines labeled and unlabeled data.
What is Semi-supervised Learning ?
Semi-supervised Learning is a machine learning technique that combines both labeled and unlabeled data to improve model performance. It is useful when acquiring labeled data is expensive or time-consuming, but unlabeled data is readily available. In this approach, the model is first trained on a small amount of labeled data and then refined using a larger amount of unlabeled data.
How Semi-supervised Learning Works
- Labeled Data: A small subset of data where the correct output (label) is known.
- Unlabeled Data: A larger subset of data where the outputs are not known.
- Learning Process: The model initially learns from the labeled data and then leverages the patterns in the unlabeled data to make more accurate predictions.
Applications of Semi-supervised Learning
- Medical Imaging: Semi-supervised learning can be used to classify images, such as identifying tumors in X-rays or MRIs, where labeled data may be scarce but a large amount of unlabeled medical images are available.
- Speech Recognition: Semi-supervised learning is often applied to speech data, where it can be difficult to label every speech sample, but there is a large amount of unlabeled speech data.
- Text Classification: In NLP tasks, semi-supervised learning can improve text classification , sentiment analysis , or topic modeling when labeled data is limited.
Example of Semi-supervised Learning
In image classification, suppose you have a small set of labeled images (e.g., 100 images of cats and dogs) but thousands of unlabeled images. Using semi-supervised learning, a model can be trained on the labeled images to learn general patterns and then refine its predictions by using the unlabeled images. This helps the model generalize better and make accurate predictions on new data without needing a large amount of labeled examples.