| [1] | Johnson AR, Wiens JA, Milne BT, Crist TO (1992) Animal movements and population dynamics in heterogeneous landscapes. Landscape Ecology 7: 63–75.
|
| [2] | With KA (1994) Using fractal analysis to assess how species perceive landscape structure. Landscape Ecology 9: 25–36.
|
| [3] | Nams V (1996) The VFractal : a new estimator for fractal dimension of animal movement paths. Landscape Ecology 11: 289–297.
|
| [4] | Bergman CM, Schaefer JA, Luttich SN (2000) Caribou movement as a correlated random walk. Oecologia 123: 364–374.
|
| [5] | Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, et al. (2008) A movement ecology paradigm for unifying organismal movement research. PNAS 105: 19052–19059.
|
| [6] | Patterson TA, Thomas L, Wilcox C, Ovaskainen O, Matthiopoulos J (2008) State-space models of individual animal movement. Trends in Ecology and Evolution 23: 87–94.
|
| [7] | Hart T, Mann R, Coulson T, Pettorelli N, Trathan P (2010) Behavioural switching in a central place forager: patterns of diving behaviour in the macaroni penguin (Eudyptes chrysolophus). Marine Biology 157: 1543–1553.
|
| [8] | Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, et al. (2012) Flexible and practical modeling of animal telemetry data : hidden Markov models and extensions. Ecology 93: 2336–2342.
|
| [9] | Dean B, Freeman R, Kirk H, Leonard K, Phillips RA, et al.. (2012) Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour. Journal of the Royal Society, Interface/the Royal Society 20120570.
|
| [10] | Peel D, Good NM (2011) A hidden Markov model approach for determining vessel activity from vessel monitoring system data. Canadian Journal of Fisheries and Aquatic Sciences 68: 1252–1264.
|
| [11] | Walker E, Bez N (2010) A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data. Ecological Modelling 221: 2008–2017.
|
| [12] | Vermard Y, Rivot E, Mahévas S, Marchal P, Gascuel D (2010) Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian hidden Markov models. Ecological Modelling 221: 1757–1769.
|
| [13] | Bestley S, Patterson TA, Hindell MA, Gunn JS (2010) Predicting feeding success in a migratory predator: integrating telemetry, environment, and modeling techniques. Ecology 91: 2373–2384.
|
| [14] | Pedersen MW, Patterson TA, Thygesen UH, Madsen H (2011) Estimating animal behavior and residency from movement data. Oikos 120: 1281–1290.
|
| [15] | Patterson TA, Basson M, Bravington MV, Gunn JS (2009) Classifying movement behaviour in relation to environmental conditions using hidden Markov models. The Journal of animal ecology 78: 1113–1123.
|
| [16] | Jonsen ID, Myers RA, James MC (2007) Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model. Marine Ecology Progress Series 337: 255–264.
|
| [17] | Franke A, Caelli T, Hudson RJ (2004) Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models. Ecological Modelling 173: 259–270.
|
| [18] | Franke A, Caelli T, Kuzyk G, Hudson RJ (2006) Prediction of wolf (Canis lupus) kill-sites using hidden Markov models. Ecological Modelling 197: 237–246.
|
| [19] | Roberts S, Guilford T, Rezek I, Biro D (2004) Positional entropy during pigeon homing I: application of Bayesian latent state modelling. Journal of theoretical biology 227: 39–50.
|
| [20] | Guilford T, Roberts S, Biro D, Rezek I (2004) Positional entropy during pigeon homing II: navigational interpretation of Bayesian latent state models. Journal of theoretical biology 227: 25–38.
|
| [21] | Lau KK, Roberts S, Biro D, Freeman R, Meade J, et al. (2006) An edge-detection approach to investigating pigeon navigation. Journal of theoretical biology 239: 71–8.
|
| [22] | Mann RP, Perna A, Str？mbom D, Garnett R, Herbert-Read JE, et al. (2013) Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection. PLoS Computational Biology 9: e1002961.
|
| [23] | Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77: 257–286.
|
| [24] | Gimpel K, Rudoy D (2008) Statistical Inference in Graphical Models. Technical report, Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts.
|
| [25] | Tan CO, ？zesmi U, Beklioglu M, Per E, Kurt B (2006) Predictive models in ecology: Comparison of performances and assessment of applicability. Ecological Informatics 1: 195–211.
|
| [26] | Hijmans RJ (2012) Cross-validation of species distribution models : removing spatial sorting bias and calibration with a null model. Ecology 93: 679–688.
|
| [27] | Peraltilla S, Bertrand S In situ measurements of the speed of peruvian anchovy schools.
|
| [28] | Bertrand S, Bertrand A, Guevara-Carrasco R, Gerlotto F (2007) Scale-invariant movements of fishermen: The same foraging strategy as natural predators. Ecological Applications 17: 331–337.
|
| [29] | Bertrand S, Burgos JM, Gerlotto F, Atiquipa J (2005) Lévy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy (Engraulis ringens). ICES Journal of Marine Science 62: 477–482.
|
| [30] | Bertrand S, Joo R, Smet CA, Tremblay Y, Weimerskirch H (2012) Local depletion by a fishery can affect seabird foraging. Journal of Applied Ecology.
|
| [31] | Cook TR, Hamann M, Pichegru L, Bonadonna F, Grémillet D, et al. (2012) GPS and time-depth loggers reveal underwater foraging plasticity in a ying diver, the Cape Cormorant. Marine Biology 159: 373–387.
|
| [32] | Dragon A, Bar-Hen A, Monestiez P, Guinet C (2012) Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data. Marine Ecology Progress Series 452: 253–267.
|
| [33] | Heaslip SG, Iverson SJ, Bowen WD, James MC (2012) Jellyfish support high energy intake of leatherback sea turtles (Dermochelys coriacea): video evidence from animal-borne cameras. PloS one 7: e33259.
|
| [34] | Thiebault A, Tremblay Y (in press) Splitting animal trajectories into fine-scale behaviorally consistent movement units: breaking points relate to external stimuli in a foraging seabird.
|
| [35] | Wilson R, Shepard E, N L (2008) Prying into the intimate details of animal lives: use of a daily diary on animals. Endang Species Res 4: 123–137.
|
| [36] | Dietterich TG (2002) Machine learning for sequential data: A review. In: Structural, Syntactic, and Statistical Pattern Recognition. Springer-Verlag, 15–30.
|
| [37] | Chapelle O, Sch？lkopf B, Zien A (2006) Semi-Supervised Learning. Cambridge, Massachusetts.: The MIT Press.
|
| [38] | Zhang G (2000) Neural Networks for Classification: A Survey. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews 30: 451–462.
|
| [39] | Byun H, Lee SW (2002) Applications of support vector machines for pattern recognition: A survey. In: Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines. London, UK: Springer-Verlag, SVM ‘02, 213–236. URL http://dl.acm.org/citation.cfm?id=647230？.719394.
|
| [40] | Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, et al. (2007) Random forests for classification in ecology. Ecology 88: 2783–2792.
|
| [41] | Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction. Springer, 2 edition. URL http://www-stat.stanford.edu/tibs/ElemSt？atLearn/.
|
| [42] | Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66: 247–259.
|
| [43] | Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology 83: 171–193.
|
| [44] | Crisci C, Ghattas B, Perera G (2012) A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling 240: 113–122.
|
| [45] | Morales JM, Fortin D, Frair JL, Merrill EH (2005) Adaptive models for large herbivore movements in heterogeneous landscapes. Landscape Ecology 20: 301–316.
|
| [46] | Bertrand S, Diaz E, Lengaigne M (2008) Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data. Progress In Oceanography 79: 379–389.
|
| [47] | Joo R, Bertrand S, Chaigneau A, ？iquen M (2011) Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery. Ecological Modelling 222: 1048–1059.
|
| [48] | Chavez F, Bertrand A, Guevara-Carrasco R, Soler P, Csirke J (2008) The northern Humboldt Current System: Brief history, present status and a view towards the future. Progress in Oceanography 79: 95–105.
|
| [49] | Bertrand S, Dewitte B, Tam J, Díaz E, Bertrand A (2008) Impacts of Kelvin wave forcing in the Peru Humboldt Current system: Scenarios of spatial reorganizations from physics to fishers. Progress in Oceanography 79: 278–289.
|
| [50] | Nallapati R (2004) Discriminative models for information retrieval. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM, SIGIR ‘04, 64–71. doi:10.1145/1008992.1009006. URL http://doi.acm.org/10.1145/1008992.10090？06.
|
| [51] | He X, Deng L, Chou W (2008) Discriminative Learning in Sequential Pattern Recognition. IEEE Signal Processing Magazine 14: 14–36.
|
| [52] | Mjolsness E, DeCoste D (2001) Machine learning for science: state of the art and future prospects. Science (New York, NY) 293: 2051–5.
|
| [53] | Bengio Y (1999) Markovian models for sequential data. Neural Computing Surveys 2: 129–162.
|
| [54] | Schwarz G (1978) Estimating the dimension of a model. The Annals of Statistics 6: 461–464.
|
| [55] | Akaike H (1981) Likelihood of a model and information criteria. Journal of Econometrics 16: 3–14.
|
| [56] | Murphy K (1998). Hidden markov model toolbox for matlab. URL http://www.ai.mit.edu/murphyk/Software/h？mm.html/.
|
| [57] | Guédon Y (2003) Estimating hidden semi-Markov chains from discrete sequences. Journal of Computational and Graphical Statistics 12: 604–639.
|
| [58] | Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing 21: 2248–2266.
|
| [59] | Yu Sz (2010) Hidden semi-Markov models. Artificial Intelligence 174: 215–243.
|
| [60] | Levinson S (1986) Continuously variable duration hidden markov models for automatic speech recognition. Computer Speech & Language 1: 29–45.
|
| [61] | Beyreuther M, Wassermann J (2011) Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers. Nonlinear Processes in Geophysics 18: 81–89.
|
| [62] | Guédon Y (2007) Exploring the state sequence space for hidden Markov and semi-Markov chains. Computational Statistics & Data Analysis 51: 2379–2409.
|
| [63] | Breiman L (2001) Random forests. Machine Learning 45: 5–32.
|
| [64] | Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2: 121–167.
|
| [65] | Warner B, Misra M (1996) Understanding neural networks as statistical tools. The American Statistician 50: 284–293.
|
| [66] | Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55: 169–186.
|
| [67] | Jaiantilal A (2009). Classification and regression by randomforest-matlab. URL http://code.google.com/p/randomforest-ma？tlab.
|
| [68] | Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2: 27 1–27: 27.
|
| [69] | McSherry F, Najork M (2008) Computing information retrieval performance measures efficiently in the presence of tied scores. In: Macdonald C, Ounis I, Plachouras V, Ruthven I, White R, editors, Advances in Information Retrieval, Springer Berlin/Heidelberg, volume 4956 of Lecture Notes in Computer Science. 414–421. URL http://dx.doi.org/10.1007/978-3-540-7864？6-7_38.
|
| [70] | Kohavi R, Provost F (1998) Glossary and terms. Maching Learning 30: 271–274.
|
| [71] | Siegel S (1956) Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill.
|
| [72] | Whitehead H, Jonsen ID (2013) Inferring animal densities from tracking data using markov chains. PloS one 8: e60901.
|
| [73] | Jonsen I, Basson M, Bestley S, Bravington M, Patterson T, et al. (2013) State-space models for bio-loggers: A methodological road map. Deep-Sea Research Part II 88–89: 34–46.
|
| [74] | Sacks J, Ylvisaker D (2012) After 50+ Years in Statistics, An Exchange. Statistical Science 27: 308–318.
|
| [75] | Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39: 1–38.
|
| [76] | Heskes T (1996) WigerinckW (1996) A theoretical comparison of batch-mode, on-line, cyclic, and almostcyclic learning. IEEE transactions on neural networks 7: 919–25.
|
| [77] | Bourlard H, Morgan N (1994) Connectionist speech recognition: A hybrid approach. Kluwer Academic Publishers.
|
| [78] | Bourlard H, Morgan N (1998) Hybrid HMM/ANN systems for speech recognition : Overview and new research directions. In: Adaptive Processing of Sequences and Data Structures, ser. Lecture Notes in Artificial Intelligence.
|
| [79] | Ganapathiraju A, Hamaker J, Picone J (2000) Hybrid SVM/HMM architectures for speech recognition. ICSLP 4: 504–507.
|
| [80] | Stadermann J, Rigoll G (2004) A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition. In: ICSLP - INTERSPEECH. Jeju Island, Korea.
|
