TY  - JOUR
A1  - Kloft, Marius
A1  - Blanchard, Gilles
T1  - On the Convergence Rate of l(p)-Norm Multiple Kernel Learning
T2  - JOURNAL OF MACHINE LEARNING RESEARCH
N2  - We derive an upper bound on the local Rademacher complexity of l(p)-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous local approaches analyzed the case p - 1 only while our analysis covers all cases 1 <= p <= infinity, assuming the different feature mappings corresponding to the different kernels to be uncorrelated. We also show a lower bound that shows that the bound is tight, and derive consequences regarding excess loss, namely fast convergence rates of the order O( n(-)1+alpha/alpha where alpha is the minimum eigenvalue decay rate of the individual kernels.
KW  - multiple kernel learning
KW  - learning kernels
KW  - generalization bounds
KW  - local Rademacher complexity
Y1  - 2012
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/35715
SN  - 1532-4435
VL  - 13
SP  - 2465
EP  - 2502
PB  - MICROTOME PUBL
CY  - BROOKLINE
ER  -