A unified framework for modeling and predicting going-out behavior

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7319 LNCS, Issue , 2012, Pages 73-90

  Tominaga, Shoji ^a   Shimosaka, Masamichi ^a   Fukui, Rui ^a   Sato, Tomomasa ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Activity recognition; Location representation; Methodology

Indexed keywords

ACTIVITY RECOGNITION; CLUSTERING METHODS; MEDICAL SCIENCE; METHODOLOGY; NON-PARAMETRIC; PREDICTION METHODS; TIME HISTORY; UNIFIED FRAMEWORK;

ARTIFICIAL INTELLIGENCE;

UBIQUITOUS COMPUTING;

EID: 84864276299     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31205-2_5     Document Type: Conference Paper

References (18)

1. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Proceedings of the Second International Symposium on Information Theory, pp. 267-281 (1973)
2. Antoniak, C.E.: Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics 2(6), 1152-1174 (1974)
3. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
4. Blei, D.M., Jordan, M.I.: Variational inference for Dirichlet process mixtures. International Society for Bayesian Analysis 1, 121-144 (2006)
5. Farrahi, K., Gatica-Perez, D.: Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology 2, 3:1-3:27 (2011)
6. Gill, J., Hangartner, D.: Circular data in political science and how to handle it. Political Analysis 18(3) (2010)
7. Gupta, M., Intille, S.S., Larson, K.: Adding GPS-Control to Traditional Thermostats: An Exploration of Potential Energy Savings and Design Challenges. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 95-114. Springer, Heidelberg (2009)
8. Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying Poisson processes. In: Proceedings of Knowledge Discovery and Data Mining, pp. 207-216 (2006)
9. Ishwaran, H., James, L.F.: Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association 96, 161-174 (2001)
10. Kono, A., Kai, I., Sakato, C., Rubenstein, L.Z.: Frequency of going outdoors predicts long-range functional change among ambulatory frail elders living at home. Archives of Gerontology and Geriatrics 45(3), 233-242 (2007)
11. Krumm, J., Brush, A.J.B.: Learning Time-Based Presence Probabilities. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 79-96. Springer, Heidelberg (2011)
12. Maceachern, S.N.: Estimating normal means with a conjugate style Dirichlet process prior. Communications in Statistics B 23, 727-741 (1994)
13. Noguchi, H., Urushibata, R., Sato, T., Mori, T., Sato, T.: System for Tracking Human Position by Multiple Laser Range Finders Deployed in Existing Home Environment. In: Lee, Y., Bien, Z.Z., Mokhtari, M., Kim, J.T., Park, M., Kim, J., Lee, H., Khalil, I. (eds.) ICOST 2010. LNCS, vol. 6159, pp. 226-229. Springer, Heidelberg (2010)
14. Rashidi, P., Cook, D.J.: Mining sensor streams for discovering human activity patterns over time. In: Proceedings of International Conference on Data Mining, pp. 431-440 (2010)
15. Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461-464 (1978)
16. Scott, J., Brush, A.J.B., Krumm, J., Meyers, B., Hazas, M., Hodges, S., Villar, N.: PreHeat: controlling home heating using occupancy prediction. In: Proceedings of the International Conference on Ubiquitous Computing, pp. 281-290 (2011)
17. Sethuraman, J.: A constructive definition of Dirichlet priors. Statistica Sinica 4, 639-650 (1994)
18. Shimosaka, M., Ishino, T., Noguchi, H., Sato, T., Mori, T.: Detecting human activity profiles with Dirichlet enhanced inhomogeneous Poisson processes. In: Proceedings of International Conference on Pattern Recognition, pp. 4384-4387 (2010)