185 citations · 945 across the 83 of their papers we have counts for
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Fast Learning Rate of Non-Sparse Multiple Kernel Learning and Optimal Regularization Strategies
Taiji Suzuki
In this paper, we give a new generalization error bound of Multiple Kernel Learning (MKL) for a general class of regularizations, and discuss what kind of regularization gives a fa…
Relative Density-Ratio Estimation for Robust Distribution Comparison
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori +2
Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully app…
Fast Learning Rate of lp-MKL and its Minimax Optimality
Taiji Suzuki
In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instea…
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…
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_…