Expectation-Maximization Algorithm
Machine Learning
An iterative algorithm for finding maximum likelihood estimates in models with latent variables.
Detailed Explanation
Expectation-Maximization (EM) is an iterative algorithm used in machine learning to find maximum likelihood estimates when models involve hidden or unobserved data. It alternates between estimating the missing data (E-step) and optimizing model parameters (M-step), gradually improving the likelihood until convergence. EM is widely used for clustering, mixture models, and incomplete data problems.
Use Cases
- •Used for clustering customer segments in marketing with incomplete data, improving model accuracy through iterative expectation and maximization steps.
Related Terms
Other terms in the Machine Learning category