Das Baum-Welch Algorithmus is a statistical algorithm used in the field of Künstliche Intelligenz and Maschinelles Lernen to perform Parameterschätzung for Versteckte Markov-Modelle (HMMs). It is a specific case of the Expectation-Maximization (EM) algorithm, which seeks to find the unknown parameters of a statistical model given incomplete data.
In vielen Anwendungen, wie Spracherkennung, biological sequence analysis, and financial modeling, the underlying processes are not directly observable. HMMs provide a framework for modeling such systems, where the model consists of hidden states and observable outputs. The Baum-Welch Algorithm allows practitioners to improve their HMMs by refining the estimates of the model parameters (such as transition probabilities and emission probabilities) based on the observed sequences of data.
Der Algorithmus arbeitet in zwei Hauptschritten: dem Erwartungsschritt (E-Schritt) und dem Maximierungsschritt (M-Schritt).
- E-Schritt: In this step, the algorithm calculates the Erwartungswert of the log-likelihood function, given the current estimates of the model parameters.
- M-Schritt: Here, the algorithm updates the model parameters to maximize the expected log-likelihood found in the E-step.
The process of iterating between these two steps continues until convergence, meaning that the changes in the parameter estimates fall below a predefined threshold. The Baum-Welch Algorithm is particularly powerful because it can handle large datasets and complex models, making it a popular choice in various KI-Anwendungen.