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T. Kleinberger, M. Becker, E. Ras, A. Holzinger, P. Müller, in Ambient Intelligence in Assisted Liv-
ing: Enable Elderly People to Handle Future Interfaces . Universal Access in Human-Computer
Interaction, Ambient Interaction, 2007
H.W. Kuhn, A.W. Tucker et al., in Nonlinear Programming . Berkeley Symposium on Mathematical
Statistics and Probability (1951)
N. Landwehr, M. Hall, E. Frank, Logistic model trees. Mach. Learn. 59 , 161-205 (2005)
N.D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A.T. Campbell, A survey of mobile phone
sensing. IEEE Commun. Mag. 48 , 140-150 (2010)
N. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, A.
Campbell, in Bewell: A Smartphone Application to Monitor, Model and Promote Wellbeing . IEEE
International ICST Conference on Pervasive Computing Technologies for Healthcare, 2012
O. Lara, M. Labrador, A survey on human activity recognition using wearable sensors. IEEE Com-
mun. Surv. Tut. 1 , 1-18 (2012)
Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backprop-
agation applied to handwritten zip code recognition. Neural Comput. 1 , 541-551 (1989)
A. Mannini, A.M. Sabatini, Machine learning methods for classifying human physical activity from
on-body accelerometers. Sensors 10 , 1154-1175 (2010)
K.P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012)
B. Najafi, K. Aminian, F. Loew, Y. Blanc, P.A. Robert, Measurement of stand-sit and sit-stand
transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly.
IEEE Trans. Biomed. Eng. 49 , 843-851 (2002)
M. Ogawa, R. Suzuki, S. Otake, T. Izutsu, T. Iwaya, T. Togawa, in Long Term Remote Behavioral
Monitoring of Elderly by Using Sensors Installed in Ordinary Houses . International IEEE-EMBS
Special Topic Conference on Microtechnologies in Medicine and Biology, 2002
M.W. Oliphant, The mobile phone meets the internet. IEEE Spectr. 36 , 20-28 (1999)
L. Oneto, N. Greco, Model selection for support vector machines: advantages and disadvantages
of the machine learning theory. Master's thesis, Department of Biophysical and Electronic Engi-
neering (2010)
J. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines
(Technical report, Microsoft Research, 1998)
R. Poppe, Vision-based human motion analysis: an overview. Comput. Vis. Image Underst. 108 ,
4-18 (2007)
J.R. Quinlan, Induction of decision trees. Mach. learn. 1 , 81-106 (1986)
J.R. Quinlan, C4. 5: Programs for Machine Learning (Morgan kaufmann, San Francisco, 1993)
N. Ravi, D. Nikhil, P. Mysore, M.L. Littman, in Activity Recognition from Accelerometer Data .
Innovative Applications of Artificial Intelligence (2005)
R. Rifkin, A. Klautau, In defense of one-vs-all classification. J. Mach. Learn Res. 5 , 101-141 (2004)
S. Shalev-Shwartz, Online learning and online convex optimization. Found. Trends Mach. Learn.
4 , 107-194 (2011)
J. Shawe-Taylor, S. Sun, A review of optimization methodologies in support vector machines.
Neurocomputing 74 , 3609-3618 (2011)
R.S. Sutton, A.G. Barto, Reinforcement learning: An introduction (Cambridge University Press,
Cambridge, 1998)
B. Takac, A. Català, D.R Martín, N. van der Aa, W. Chen, M. Rauterberg, Position and orientation
tracking in a ubiquitous monitoring system for parkinson disease patients with freezing of gait
symptom. J. Med. Int. Res. 15 , 1 (2013)
E. Tapia, S. Intille, L. Lopez, K.Larson, in Newblock. The Design of a Portable Kit of Wireless
Sensors for Naturalistic Data Collection . Pervasive Computing, 2006
S. Vijayakumar, T. Shibata, S. Schaal, in Reinforcement Learning for Humanoid Robotics .
Autonomous Robot, 2003
J. Weston, C. Watkins, Multi-class support vector machines (University of London, Department of
Computer Science, Technical report, 1998)
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