Hierarchical Reinforcement Learning
Machine Learning
A statistical model where the system is assumed to be a Markov process with hidden states that generate observable outputs.
Detailed Explanation
Hidden Markov Models (HMMs) are statistical models used in machine learning to represent systems with unobservable (hidden) states. They assume that the system transitions between these states probabilistically, and each state produces observable outputs with certain likelihoods. HMMs are widely used in speech recognition, natural language processing, and bioinformatics for modeling temporal or sequential data.
Use Cases
- •Used to transcribe speech by modeling sequential audio patterns to improve accuracy in speech recognition systems.
Related Terms
Other terms in the Machine Learning category