The objective of supervised learning is to find an input-output relationship behind training samples (Bishop, 2006; Hastie, Tibshirani, & Friedman, 2001). Once the input-output relationship is successfully learned, outputs for unseen inputs can be predicted, i.e., the learning machine can generalize. When users are allowed to choose the location of training inputs, it is desirable to design the input locations so that the generalization error is minimized. Such a problem is called active learning (Settles, 2009) or experiment design (Fedorov, 1972; Pukelsheim, 1993), and has been shown to be useful in various application areas such as text classification (Lewis & Gale, 1994; McCallum & Nigam, 1998), age estimation from images (Ueki, Sugiyama, & Ihara, 2010), medical data analysis (Wiens & Guttag, 2010), chemical data analysis (Warmuth et al., 2003), biological data analysis (Liu, 2004), and robot control (Akiyama, Hachiya, & Sugiyama, 2010).
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