![](bgc.png)
of kernel matrices.
ACKNOWLEDGEMENTS
The authors acknowledge the support of Deutsche
Forschungsgemeinschaft (DFG) within the Collabo-
rative Research Center SFB 876 “Providing Informa-
tion by Resource-Constrained Analysis”, projects A1
and C1.
REFERENCES
Bache, K. and Lichman, M. (2013). UCI machine learning
repository.
Bertsekas, D. P. and Tsitsiklis, J. N. (1997). Parallel
and Distributed Computation: Numerical Methods.
Athena Scientific.
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A
training algorithm for optimal margin classifiers. In
Proceedings of the fifth Annual Workshop on Compu-
tational Learning Theory, pages 144–152.
Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J.
(2011). Distributed optimization and statistical learn-
ing via the alternating direction method of multipliers.
Foundations and Trends in Machine Learning, 3(1):1–
122.
Cand`es, E. J. and Recht, B. (2009). Exact matrix comple-
tion via convex optimization. Foundations of Compu-
tational Mathematics, 9(6):717–772.
Cand`es, E. J. and Recht, B. (2012). Exact matrix comple-
tion via convex optimization. Communications of the
ACM, 55(6):111–119.
Crammer, K., Dredze, M., and Pereira, F. (2012).
Confidence-weighted linear classification for natural
language processing. Journal of Machine Learning
Research, 13:1891–1926.
Forero, P. A., Cano, A., and Giannakis, G. B. (2010).
Consensus-based distributed support vector machines.
Journal of Machine Learning Research, 11:1663–
1707.
Ji, Y. and Sun, S. (2013). Multitask multiclass support vec-
tor machines: Model and experiments. Pattern Recog-
nition, 46(3):914–924.
Joachims, T. (1999). Making large-scale support vector ma-
chine learning practical. In Sch¨olkopf, B., Burges, C.,
and Smola, A., editors, Advances in Kernel Methods -
Support Vector Learning, chapter 11, pages 169–184.
MIT Press, Cambridge, MA.
Lanckriet, G., Cristianini, N., Bartlett, P., E. G., and L.,
Jordan, M. (2002). Learning the kernel matrix with
semidefinite programming. In Proceedings of the 19th
International Conference on Machine Learning.
Lanckriet, G. R. G., De Bie, T., Cristianini, N., Jor-
dan, M. I., and Noble, W. S. (2004). A statistical
framework for genomic data fusion. Bioinformatics,
20(16):2626–2635.
Lee, S. and Bockermann, C. (2011). Scalable stochastic
gradient descent with improved confidence. In Big
Learning – Algorithms, Systems, and Tools for Learn-
ing at Scale, NIPS Workshop.
Lee, S., Stolpe, M., and Morik, K. (2012). Separable ap-
proximate optimization of support vector machines
for distributed sensing. In Flach, P., Bie, T., and
Cristianini, N., editors, Machine Learning and Knowl-
edge Discovery in Databases, volume 7524 of Lecture
Notes in Computer Science, pages 387–402. Springer.
Lippi, M., Bertini, M., and Frasconi, P. (2010). Collective
traffic forecasting. In Proceedings of the 2010 Euro-
pean conference on Machine learning and knowledge
discovery in databases: Part II, pages 259–273.
Morik, K., Bhaduri, K., and Kargupta, H. (2012). Intro-
duction to data mining for sustainability. Data Mining
and Knowledge Discovery, 24(2):311–324.
Rakotomamonjy, A., Bach, F., Canu, S., and Grandvalet., Y.
(2007). More efficiency in multiple kernel learning. In
Proceedings of the 24th International Conference on
Machine Learning.
Recht, B., Fazel, M., and Parrilo, P. A. (2010). Guaran-
teed minimum-rank solutions of linear matrix equa-
tions via nuclear norm minimization. SIAM Review,
52(3):471–501.
Recht, B. and R´e, C. (2011). Parallel stochastic gradient al-
gorithms for large-scale matrix completion. Technical
report, University of Wisconsin-Madison.
Schoenberg, I. J. (1938). Metric spaces and positive definite
functions. Transactions of the American Mathemati-
cal Society, 44(3):522–536.
Scholkopf, B. and Smola, A. J. (2001). Learning with Ker-
nels: Support Vector Machines, Regularization, Opti-
mization, and Beyond. MIT Press, Cambridge, MA,
USA.
Shawe-Taylor, J. and Sun, S. (2011). A review of optimiza-
tion methodologies in support vector machines. Neu-
rocomputing, 74(17):3609–3618.
Stolpe, M., Bhaduri, K., Das, K., and Morik, K. (2013).
Anomaly detection in vertically partitioned data by
distributed core vector machines. In Machine Learn-
ing and Knowledge Discovery in Databases - Euro-
pean Conference, ECML PKDD 2013.
Whittaker, J., Garside, S., and Lindveld, K. (1997). Track-
ing and predicting a network traffic process. Interna-
tional Journal of Forecasting, 13(1):51–61.
ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods
124