6 citations · 10 across the 7 of their papers we have counts for
5 papers · 1 filter
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Kota Matsui, Wataru Kumagai, Takafumi Kanamori
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required…
Breakdown Point of Robust Support Vector Machine
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
The support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has a serious drawback, that is sen…
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems
Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
In binary classification problems, mainly two approaches have been proposed; one is loss function approach and the other is uncertainty set approach. The loss function approach is…
Semi-Supervised learning with Density-Ratio Estimation
Masanori Kawakita, Takafumi Kanamori
In this paper, we study statistical properties of semi-supervised learning, which is considered as an important problem in the community of machine learning. In the standard superv…
f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
A density ratio is defined by the ratio of two probability densities. We study the inference problem of density ratios and apply a semi-parametric density-ratio estimator to the tw…