FEATURE SELECTION IN ADAPTIVE REGULARIZATION OF WEIGHT VECTORS VIA SPARSE ESTIMATION

DOI Web Site

Bibliographic Information

Other Title
  • スパース推定に基づく適応正則化オンライン学習の特徴選択問題
  • スパース スイテイ ニ モトズク テキオウ セイソクカ オンライン ガクシュウ ノ トクチョウ センタク モンダイ

Search this article

Abstract

This paper focuses on the on-line learning method called Adaptive Regularization of Weight Vectors (AROW). AROW has two advantages compared to other on-line learning methods. First, it is robust to label noise. Second, we can obtain stable models by taking confidence intervals of parameters into consideration. However, AROW cannot perform feature selection. This paper proposes a novel method by combining AROW with lasso. We also employ the coordinate descent algorithm to estimate parameters, which enables us to speed up our algorithm. We confirm the effectiveness of our proposed method by some numerical experiments.

Journal

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top