Semi-Supervised Learning is a machine learning approach that falls between supervised and unsupervised learning. It utilizes a small amount of labeled data along with a large amount of unlabeled data to improve learning efficiency and accuracy.
Types of Semi-Supervised Learning:
Self-Training
A model is first trained on labeled data, then used to label some of the unlabeled data, which is added back for further training.
Example: A spam detection model is trained on a small set of labeled emails and then used to label additional emails, improving accuracy over time.
Co-Training
Two models are trained on different feature sets of the same data and help label unlabeled data for each other.
Example: A face recognition system might use both image pixels and metadata (e.g., timestamps) as separate features to learn better.
Graph-Based Methods
Data points are represented as nodes in a graph, and labels propagate through connected nodes.
Example: Social media friend recommendations use graph structures to infer new connections based on labeled users' preferences.
Generative Models
A probabilistic model is trained to understand data distribution and generate labels for unlabeled data.
Example: A model trained on a few labeled medical images generates synthetic labels for new images.
Use Cases:
Healthcare: Medical diagnosis with a limited number of labeled cases.
Speech Recognition: Using labeled audio data and unlabeled speech recordings to improve accuracy.
Text Classification: Sentiment analysis with a few labeled reviews and a vast number of unlabeled ones.
No comments:
Post a Comment