8 citations · 13 across the 4 of their papers we have counts for
4 papers
Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems
Shota Okumura, Yoshiki Suzuki, Ichiro Takeuchi
We introduce a novel sensitivity analysis framework for large scale classification problems that can be used when a small number of instances are incrementally added or removed. Fo…
Regularization Path of Cross-Validation Error Lower Bounds
Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama +1
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances. Nevertheless, curre…
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models
Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura +1
Practical model building processes are often time-consuming because many different models must be trained and validated. In this paper, we introduce a novel algorithm that can be u…
Safe Sample Screening for Support Vector Machines
Kohei Ogawa, Yoshiki Suzuki, Shinya Suzumura +1
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vec…