most citedRegularization Strategies and Empirical Bayesian Learning for MKL

13 citations · 15 across the 3 of their papers we have counts for

collaborators

3 papers

stat.ML2011

Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization

Taiji Suzuki, Ryota Tomioka, Masashi Sugiyama

We theoretically investigate the convergence rate and support consistency (i.e., correctly identifying the subset of non-zero coefficients in the large sample limit) of multiple ke…

stat.ML2011★ 2 cited

Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization

Taiji Suzuki, Ryota Tomioka, Masashi Sugiyama

We investigate the learning rate of multiple kernel leaning (MKL) with elastic-net regularization, which consists of an -regularizer for inducing the sparsity and an $\ell_…

stat.ML2010★ 13 cited

Regularization Strategies and Empirical Bayesian Learning for MKL

Ryota Tomioka, Taiji Suzuki

Multiple kernel learning (MKL), structured sparsity, and multi-task learning have recently received considerable attention. In this paper, we show how different MKL algorithms can…