Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains

Juang, Biing‐Hwang

doi:10.1002/j.1538-7305.1985.tb00273.x

Cited by 219 publications

(128 citation statements)

References 10 publications

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“…In this section we will focus on techniques based on Hidden Markov Models, which is the case of many more recent approaches 3 . Due to lack of space, our discussion of HMMs will be rather summary and heavily biased towards our application, the interested reader may refer to the papers by Juang et al [6] and Rabiner [9] for a deeper introduction.…”

Section: Motion Pattern Learning With Hmmsmentioning

confidence: 99%

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

Vasquez

Fraichard²,

Laugier³

2009

IEEE Trans. Intell. Transport. Syst.

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Summary.Modeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (eg internal state, perception, etc.) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (eg camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use off-line learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach which is able to learn new motion patterns incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models called Growing Hidden Markov models.

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Section: Motion Pattern Learning With Hmmsmentioning

confidence: 99%

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

Vasquez

Fraichard²,

Laugier³

2009

IEEE Trans. Intell. Transport. Syst.

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“…The HMM estimation algorithm is readily available in e.g., [1,2,10]. We will use the training algorithm presented in [10].…”

Section: The Hidden Markov Modelmentioning

confidence: 99%

Hierarchical Hidden Markov Models: An Application to Health Insurance Data

Tsoi

Zhang

Hagenbuchner

2006

Lecture Notes in Computer Science

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Abstract. This paper provides a constructive algorithm in which a hierarchical tree of hidden Markov models may be obtained directly from data using an unsupervised learning regime. The method is applied to health insurance transaction data such that profiles with similar local temporal behaviours are grouped together. By judicious incorporation of limited additional prior information, it is found that profiles can be separated into various sub-behavioural groups thus providing a technique for large-scale automatic labelling of data. In the application to the health insurance transaction data set, by incorporating limited information concerning the medical functions used in a medical procedure, it is possible to label some individual medical transactions as to whether they are related to a particular medical condition or not. This automatic labelling process adds values to the collected transactional database for possible further applications, e.g. public health studies.

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“…Gaussian density). The adaption of reestimation formulas of BaumWelch procedure for the continuous case is straightforward [16].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

A Hidden Markov Model-Based Approach to Sequential Data Clustering

Panuccio

Bicego

Murino

2002

Lecture Notes in Computer Science

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Abstract. Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a Hidden Markov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM to derive new proximity distances, in the likelihood sense, between sequences. Moreover, a novel partitional clustering algorithm is designed which alleviates computational burden characterizing traditional hierarchical agglomerative approaches. Experimental results show that this approach provides an accurate clustering partition and the devised distance measures achieve good performance rates. The method is demonstrated on real world data sequences, i.e. the EEG signals due to their temporal complexity and the growing interest in the emerging field of Brain Computer Interfaces.

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Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains

Cited by 219 publications

References 10 publications

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models

Hierarchical Hidden Markov Models: An Application to Health Insurance Data

A Hidden Markov Model-Based Approach to Sequential Data Clustering

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