Avtor/Urednik		Kaiser, Janez; Kalčič, Zdravko
Naslov		Učenje prikritih modelov Markova z genetskimi algoritmi
Prevedeni naslov		Training of hidden Markov models with genetic algorithms
Tip		članek
Vir		In: Zajc B, editor. Zbornik 7. elektrotehniške in računalniške konference ERK'98. Zvezek B. Računalništvo in informatika, umetna inteligenca, robotika, razpoznavanje vzorcev, biomedicinska tehnika, močnostna elektrotehnika, didaktika, študentski članki; 1998 sep 24-26; Portorož. Ljubljana: Slovenska sekcija IEEE,
Leto izdaje		1998
Obseg		str. 125-8
Jezik		slo
Abstrakt		In this paper training of Hidden Markov Models (HMM) with Genetic Algorithms (GA) is presented. Genetic algorithms are robust search and optimization techniques, based on the mechanics of evolution and natural genetics. The main aim of using GA for training of HMM's instead of common Viterbi and Baum-Welch reestimation methods is the fact that the results of the later methods axhibit strong dependency on initial conditions. As GA exploit entire search space, this dependency should be overcome. Compared with Baum-Welch method, we achieved 0.53% increase in recognition results for phonemes from database SNABI.
Deskriptorji		MARKOV CHAINS SPEECH ACOUSTICS ALGORITHMS ARTIFICIAL INTELLIGENCE CROSSES, GENETIC