@article{KloftBlanchard2012,
  author    = {Kloft, Marius and Blanchard, Gilles},
  title     = {On the Convergence Rate of l(p)-Norm Multiple Kernel Learning},
  series = {JOURNAL OF MACHINE LEARNING RESEARCH},
  volume    = {13},
  journal   = {JOURNAL OF MACHINE LEARNING RESEARCH},
  publisher = {MICROTOME PUBL},
  address   = {BROOKLINE},
  issn      = {1532-4435},
  pages     = {2465 -- 2502},
  year      = {2012},
  abstract  = {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.},
  language  = {en}
}