Semi-Supervised Learning
Semi-supervised learning uses a combination of a small amount of labeled data and a large amount of unlabeled data for training. It bridges the gap between supervised and unsupervised learning.
Understanding Semi-Supervised Learning
Semi-supervised learning combines a small amount of labeled data with a large pool of unlabeled data during training, offering a practical middle ground between supervised learning and unsupervised learning. This approach is especially valuable when labeling data is expensive or time-consuming, such as in medical imaging or natural language processing tasks. Techniques include self-training, where the model's confident predictions on unlabeled data become pseudo-labels, and consistency regularization, which enforces stable predictions under data augmentation. Graph-based methods propagate labels through data similarity structures. Semi-supervised learning has shown remarkable results, sometimes matching fully supervised performance with only a fraction of the labels, making it an essential strategy for organizations with limited annotation budgets.
Category
Machine Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Machine Learning Terms
Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.