Book organization

1. Chapter 1 (this chapter). Introduction.
2. Chapter 2 describes PDF projection, the theoretical basis of CSM.
3. Chapter 3 explains the information-theoretic aspects of PDF projection.
4. Chapter 4 provides some simple feature modules.
5. Chapter 5 presents linear feature extraction of positive exponentially-distributed data.
6. Chapter 6 presents linear feature extraction of positive truncated Gaussian-distributed data.
7. Chapter 7 presents linear feature extraction of data constrained to the interval [0,1].
8. Chapter 8 presents feature extraction by order statistics.
9. Chapter 9 presents some special feature extreaction methods.
10. Chapter 10 covers ARMA and AR feature extraction.
11. Chapter 11 covers cepstral feature extraction.
12. Chapter 12 covers various CSM topologies.
13. Chapter 13 covers feature probability density estimation methods.
14. Chapter 14 describes the multi-resolution HMM.
15. Chapter 15 describes extended feature vector (EFV) method.
16. Chapter 16 presents the projected belief network (PBN).