# Training Hidden Markov Models with Multiple Observations-A Combinatorial Method

@article{Li2000TrainingHM, title={Training Hidden Markov Models with Multiple Observations-A Combinatorial Method}, author={Xiaolin Li and Marc Parizeau and R{\'e}jean Plamondon}, journal={IEEE Trans. Pattern Anal. Mach. Intell.}, year={2000}, volume={22}, pages={371-377} }

Hidden Markov models (HMM) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a complex structure and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years…

## 163 Citations

### Training Second-Order Hidden Markov Models with Multiple Observation Sequences

- Computer Science2009 International Forum on Computer Science-Technology and Applications
- 2009

This article introduces a new HMM2 with multiple observable sequences, assuming that all the observable sequences are statistically correlated, and shows that the model training equations can be easily derived with an independence assumption.

### Learning discrete Hidden Markov Models from state distribution vectors

- Computer Science
- 2005

A new polynomial-time algorithm for supervised learning of the parameters of a first order HMM from a state probability distribution (SD) oracle and a hybrid learning algorithm for approximating HMM parameters from a dataset composed of strings and their corresponding state distribution vectors are developed.

### Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure

- Computer ScienceIHSI
- 2019

Several approaches for modifying the Baum Welch Algorithm are shown and the results of all training methods are compared.

### A novel training method for HMM2 with multiple observation sequences

- Computer Science2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)
- 2010

A novel training method for HMM2 with multiple observable sequences, assuming that all the observable sequences are driven by a common hidden sequence, and building up an associated objective function using Lagrange multiplier method.

### Generalized multi-stream hidden markov models

- Computer Science
- 2010

This dissertation developed, implement, and test multi-stream continuous and discrete hidden Markov model (HMM) algorithms that are validated on various applications including Australian Sign Language, audio classification, face classification, and more extensively on the problem of landmine detection using ground penetrating radar data.

### A generalized hidden Markov model and its applications in recognition of cutting states

- Computer Science
- 2016

A generalized hidden Markov model (GHMM) in the context of generalized interval probability theory is proposed, which provides a concise representation for the two kinds of uncertainty simultaneously.

### Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm

- Computer ScienceStatistics, Optimization & Information Computing
- 2022

The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.

### A survey of techniques for incremental learning of HMM parameters

- Computer ScienceInf. Sci.
- 2012

### PREDICTION OF FINANCIAL TIME SERIES WITH HIDDEN MARKOV MODELS

- Computer Science
- 2004

An extension of the Hidden Markov Model is developed that addresses two of the most important challenges of financial time series modeling: non-stationary and non-linearity and includes a novel exponentially weighted Expectation-Maximization (EM) algorithm to handle these two challenges.

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