The algorithm

In this chapter, the learning algorithm used for optimising the parameters of the models defined in the previous chapter is presented. The structure of this chapter is essentially the same as in the previous chapter, i.e. the HMM is discussed in Section 6.1, the NSSM in Section 6.2 and finally the switching model in Section 6.3.

All the learning algorithms are based on ensemble learning which was introduced in Section 3.3. The sections of this chapter are mostly divided into two parts, one for evaluating the ensemble learning cost function and the other for optimising it.

- Learning algorithm for the continuous density hidden Markov model

- Learning algorithm for the nonlinear state-space model
- Evaluating the cost function
- Optimising the cost function
- Learning procedure
- Continuing learning with new data

- Learning algorithm for the switching model

Antti Honkela 2001-05-30