PAC-Learning of Markov Models with Hidden State
PAC-Learning of Markov Models with Hidden State
Ricard Gavaldà,Philipp W. Keller,Joelle Pineau,Doina Precup
2006 · DOI: 10.1007/11871842_18
European Conference on Machine Learning · 27 Citations
TLDR
This work proposes a new PAC framework for learning both the topology and the parameters in partially observable Markov models, and learns a Probabilistic Deterministic Finite Automata (PDFA) which approximates a Hidden Markov Model (HMM) up to some desired degree of accuracy.