Learning
The parameter learning task in HMMs is to find, given an output sequence or a set of such sequences, the best set of state transition and output probabilities. The task is usually to derive the maximum likelihood estimate of the parameters of the HMM given the set of output sequences. No tractable algorithm is known for solving this problem exactly, but a local maximum likelihood can be derived efficiently using the Baum–Welch algorithm or the Baldi–Chauvin algorithm. The Baum–Welch algorithm is a special case of the expectation-maximization algorithm.
Read more about this topic: Hidden Markov Model
Famous quotes containing the word learning:
“The value of a family is that it cushions and protects while the individual is learning ways of coping. And a supportive social system provides the same kind of cushioning for the family as a whole.”
—Michael W. Yogman, and T. Berry Brazelton (20th century)
“Professors could silence me then; they had figures, diagrams, maps, books.... I was learning that books and diagrams can be evil things if they deaden the mind of man and make him blind or cynical before subjection of any kind.”
—Agnes Smedley (1890–1950)
“Learning without thinking is labor lost; thinking without learning is dangerous.”
—Chinese proverb.