ロボットモデル学習のためのロバスト回帰アプローチ
A Robust Regression Approach for Robot Model Learning
回帰問題における入出力マッピングの外れ値検出・除去手法を提案し、線形・非線形回帰のシミュレーションと実センサデータで有効性を示した。
著者: Francesco Cursi, Guang-Zhong Yang
分類: cs.RO, cs.LG
原文アブストラクト
Machine learning and data analysis have been used in many robotics fields, especially for modelling. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of outliers has a huge impact on modelling the acquired data, resulting in inappropriate models. In this work a novel approach for outlier detection and rejection for input/output mapping in regression problems is presented. The robustness of the method is shown both through simulated data for linear and nonlinear regression, and real sensory data. Despite being validated by using artificial neural networks, the method can be generalized to any other regression method
