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      Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus)


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          We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.

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          A tutorial on hidden Markov models and selected applications in speech recognition

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            Training a support vector machine in the primal.

            Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.
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              Kernel Methods for Pattern Analysis


                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                19 November 2012
                : 7
                : 11
                : e49120
                [1 ]Computational Statistics and Machine Learning, University College London, London, United Kingdom
                [2 ]Department of Computer Science, University College London, London, United Kingdom
                [3 ]Botswana Predator Conservation Trust, Maun, Botswana
                [4 ]Wildlife Conservation Research Unit (WildCRU), Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Oxford, United Kingdom
                [5 ]Structure and Motion Laboratory, The Royal Veterinary College, University of London, London, United Kingdom
                Tulane University Medical School, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: FB DM JM. Performed the experiments: FB. Analyzed the data: SG FB JS SH. Contributed reagents/materials/analysis tools: DM JM AW SH. Wrote the paper: SG FB DM AW JM JS SH.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                : 5 July 2012
                : 8 October 2012
                Page count
                Pages: 11
                The authors had financial support from numerous donors including the Tom Kaplan Prize Scholarship, the PG Allen Family Foundation, the Lee and Juliet Folger Foundation, Nathan and Rosemarie Myhrvold, Stuart and Teresa Graham, Doug and Janet True, Woodland Park Zoo, Wild Entrust International. This work was funded by the EPSRC CARDyAL: Cooperative Aerodynamics and Radio-based DYnamic Animal Localisation project, EP/H017402/1. The authors confirm that there are no additional funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Computational Biology
                Sequence Analysis
                Animal Behavior
                Computer Science
                Mathematical Computing
                Veterinary Science
                Animal Management
                Animal Behavior
                Animal Types
                Large Animals



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