OF LEARNING

2002

Citations in the corpus

Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “[5] in that it models sub-optimality in user feedback and theoretically converges to user’s hidden score function.”
On Becoming an Experiential Educator (2014, background): “The principles and concepts of experiential learning theory have been used widely to develop and deliver programs in K-12 education (McCarthy, 1996), undergraduate education (Mentkowski, 2000), and professional education (Boyatzis, Cowen, & Kolb, 1995; Reese, 1998).”
First principles of instruction (2012, background): “McCarthy (1996) stressed creating, revising, editing, synthesizing, and refocusing as important final phases of a learning experience.”
Incremental learning of gestures by imitation in a humanoid robot (2007, methodology): “The projection of the data in the latent space of motion, the classification of existing motion models, the encoding of the gestures in mixture models and the retrieval process are fully described in [6].”
Local Rademacher complexities and oracle inequalities in risk minimization (2006, background): “For ψ -learning [8], l(y, f (x) (2) holds with β = 1 /( 2 κ) , where κ is the exponent given assumption (A1).”
A Selective Overview of Variable Selection in High Dimensional Feature Space (2009, background): “The ψ -learning (Shen et al. (2003)) can also be cast in this framework.”
Novel Interaction Strategies for Learning from Teleoperation (2012, methodology): “It has been previously used in similar LfD scenarios [9] and found to be very useful.”
Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective (2012, methodology): “As mentioned previously, we use GMMs as our skill learning algorithm [10].”
Robust Truncated Hinge Loss Support Vector Machines (2007, background): “But, optimization involving the 0–1 loss is very difﬁcult (Shen, Tseng Zhang, and Wong 2003 ).”
On handling indicator constraints in mixed integer programming (2016, methodology): “In this paper, we are interested in the SVM with the ramp loss function, see [12,29].”
Full Paper Reinforcement Learning for Imitating Constrained Reaching Movements (unknown): “This application has been described in details in [14, 16].”