固定更新時間で新物体を逐次学習するロボット
Incremental Robot Learning of New Objects with Fixed Update Time
ロボットが新しい物体クラスを経験に応じて逐次追加学習できるよう、正則化最小二乗分類の逐次版を提案し、クラスごとのサンプル数が不均衡な場合でも高速かつ高精度に分類できることを示した。
著者: Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta
分類: stat.ML, cs.CV, cs.LG, cs.RO
原文アブストラクト
We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presented algorithm addresses the problem of having an unbalanced proportion of training examples per class, which occurs when new objects are presented to the system for the first time. We evaluate our algorithm on both a machine learning benchmark dataset and two challenging object recognition tasks in a robotic setting. Empirical evidence shows that our approach achieves comparable or higher classification performance than its batch counterpart when classes are unbalanced, while being significantly faster.
