Linear SVM Models for Online Activity Recognition - Smartphone-Based Human Activity Recognition - page 105

Moreover, we proposed in this chapter a novel approach for training L1-L2-SVM
classifiers. The proposed method is characterized by two main advantages: (i) it is
flexible, as it allows to solve L1, L1-L2 and L2 SVM problems and to properly tune
the trade-off between dimensionality reduction and performance; (ii) it builds on
conventional QP solvers, thus can be implemented with a minimal effort. We tested
our approach on our HAR application, that allowed us to compare the proposed
approach with state-of-the-art alternatives and to highlight the usefulness of such a
flexible solver in a real-world practical problem.
References
L. Bao, S.S. Intille, in Activity Recognition from User-Annotated Acceleration Data .Pervasive
Computing (2004)
S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University, Cambridge, 2004)
R. Fan, K. Chang, C. Hsieh, X. Wang, C. Lin, Liblinear: a library for large linear classification. J.
Mach. Learn. Res. 9 , 1871-1874 (2008)
B.P.Flannery,W.H.Press,S.A.Teukolsky,W.Vetterling, Numerical Recipes in C (Cambridge
University Press, 1992)
J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via coor-
dinate descent. J. Stat. Softw. 33 , 1 (2010)
A. Ghio, D. Anguita, L. Oneto, S. Ridella, C. Schatten, in Nested Sequential Minimal Optimization
for Support Vector Machines . International Conference on Artificial Neural Networks (2012)
D.M. Karantonis, M.R. Narayanan, M. Mathie, N.H. Lovell, B.G. Celler. Implementation of a real-
time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE
Trans. Inf. Technol. Biomed. 10 , 156-167 (2006)
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, Improvements to platt's smo algo-
rithm for svm classifier design. Neural Comput. 13 , 637-649 (2001)
N.M. Khan, R. Ksantini, I.S. Ahmad, L. Guan, A Sparse Support Vector Machine Classifier with
Nonparametric Discriminants . International Conference on Artificial Neural Networks (2012)
A.M. Khan, Y.-K. Lee, S.Y. Lee, T.-S. Kim, in Human Activity Recognition via an Accelerometer-
Enabled-Smartphone Using Kernel Discriminant Analysis . IEEE International Conference on
Future Information Technology (2010)
J. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines
(Technical report, Microsoft Research, Redmond 1998)
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes 3rd edition: The Art
of Scientific Computing (Cambridge University Press, Cambridge 2007)
R. Rifkin, A. Klautau, In defense of one-vs-all classification. J. Mach. Learn. Res. 5 , 101-141
(2004)
B. Romera-Paredes, H. Aung, N. Bianchi-Berthouze, A One-vs-One Classifier Ensemble with
Majority Voting for Activity Recognition . European Symposium on artificial neural networks,
computational intelligence and machine learning (2013)
B. Schölkopf, A.J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Opti-
mization, and Beyond (MIT press, Cambridge, 2001)
M.R. Segal, K.D. Dahlquist, B.R. Conklin, Regression approaches for microarray data analysis. J.
Comput. Biol. 10 , 961-980 (2003)
S. Shalev-Shwartz, Y. Singer, N. Srebro, in Pegasos: Primal Estimated Sub-Gradient Solver for
svm . International Conference on Machine Learning (2007)
J. Shawe-Taylor, S. Sun, A review of optimization methodologies in support vector machines.
Neurocomputing 74 , 3609-3618 (2011)