Showing all 5 Tech Reports linked to Paul Cohen

Sequence Learning via Bayesian Clustering by Dynamics

This chapter introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, the algorithm uses an entropy-based heuristic search strategy. When applied to clustering sensor data from mobile robots, the algorithm produces clusters that are meaningful in the domains of application. 1. Department...read more

ID: kmi-00-05

Date: 2000

Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen

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Multivariate Clustering by Dynamics

We present a Bayesian clustering algorithm for multivariate time series. A clustering is regarded as a probabilistic model in which the unknown auto-correlation structure of a time series is approximated by a first order Markov Chain and the overall joint distribution of the variables is simplified by conditional independence assumptions. The algorithm searches for the most probable set of clusters given the data using a entropy-based heuristic search method. The algorithm is evaluated on a set...read more

ID: kmi-00-04

Date: 2000

Author(s): Marco Ramoni, Paola Sebastiani and Paul Cohen

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Bayesian Clustering by Dynamics

This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy. An experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions. When the method is applied to...read more

ID: kmi-99-05

Date: 1999

Author(s): Marco Ramoni, Paola Sebastiani, Paul Cohen, John Warwick and James Davis

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Discovering Dynamics using Bayesian Clustering

This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy. 1. Department of Statistics, The Open University. 2. Knowledge Media...read more

ID: kmi-99-04

Date: 1999

Author(s): Paola Sebastiani, Marco Ramoni, Paul Cohen, John Warwick and James Davis

Resources:
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Bayesian Clustering of Sensory Inputs by Dynamics

This paper introduces a Bayesian method for unsupervised clustering of dynamic processes and applies it to the abstraction of sensory inputs of a mobile robot. The method starts by transforming the sensory inputs into Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the robot's experiences. To increase efficiency, the method uses an entropy-based heuristic search strategy. 1. Department of Statistics, The Open...read more

ID: kmi-99-03

Date: 1999

Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen

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Research Asst / Assoc for Blockchain Learning Verification

Knowledge Media Institute (KMi)
29,799 - 38,833 (Grade AC1 / AC2)
Based in Milton Keynes
Temporary contract for 18 months

The Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of Science, Technology, Engineering and Mathematics (STEM) at the Open University. KMi is looking for a Research Assistant or a Research Associate to work on the IoC project & how blockchains can be used to store student accreditation and lifelong learning records in order to enhance employability. The Open University leads the IoC's first theme on university learning, which aims to influence computer science...

Research Asst / Assoc for Mainstreaming Learning Analytics

Knowledge Media Institute (KMi)
29,799 - 38,833 (Grade AC1 / AC2)
Based in Milton Keynes
Temporary contract until 31st March 2021

The Knowledge Media Institute (KMi) is a distinct research unit within the Faculty of Science, Technology, Engineering and Mathematics (STEM) at the Open University. KMi is looking for a Research Assistant or a Research Associate to work on the IoC project & how we can adapt, deploy and mainstream our learning analytics tool across a number of educational establishments nationally. The Open University leads the IoC's first theme on university learning, which aims to influence computer...

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