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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
samarium cobalt a brittle, high-energy magnetic material that is best known for its performance at high temperatures, which comes in two compositions, SmCo5 and a higher energy Sm2 Co17 . sample a single measurement that is taken to be representative of the measured property over a wider area, frequency range, or time period. When recording digital sound, a sample is a voltage measurement that reflects the intensity of the acoustic signal at a particular moment, and has a time period associated with it; that is, the sample represents the signal until the next measurement is made. In a digital image, a sample is a single measurement of light intensity at a particular point in the scene, and that measurement is used to represent the actual but unmeasured intensity at nearby points. sample complexity the number of training examples required for a learning system to attain a specified learning goal. sample space the set of all possible samples of a signal, given the particular parameters of the sampling scheme. sample-and-hold fier. See sample-and-hold ampli-
Introduction
Published in Sankar K. Pal, Pabitra Mitra, Pattern Recognition Algorithms for Data Mining, 2004
Traditional machine learning algorithms deal with input data consisting of independent and identically distributed (iid) samples. In this framework, the number of samples required (sample complexity) by a class of learning algorithms to achieve a specified accuracy can be theoretically determined [19, 275]. In practice, as the amount of data grows, the increase in accuracy slows, forming the learning curve. One can hope to avoid this slow-down in learning by employing selection methods for sifting through the additional examples and filtering out a small non-iid set of relevant examples that contain essential information. Formally, active learning studies the closed-loop phenomenon of a learner selecting actions or making queries that influence what data are added to its training set. When actions/queries are selected properly, the sample complexity for some problems decreases drastically, and some NP-hard learning problems become polynomial in computation time [10, 45].
Dream to posture: visual posturing of a tendon-driven hand using world model and muscle synergies
Published in Advanced Robotics, 2023
Matthew Ishige, Tadahiro Taniguchi, Yoshihiro Kawahara
It is well known that kinematic and mechanical models of anthropomorphic hands are nonlinear and complex [4,30]. Therefore, learning-based approaches have been investigated for controlling these manipulators. Reinforcement learning is a common approach for realizing controllers for complex manipulators [7–11]. Learning from a demonstration is another popular and data-efficient approach that uses demonstration data from human experts to realize controllers [12,13]. To overcome the sample complexity of controller learning, especially in reinforcement learning, sim2real is often employed, where a controller is first trained in a simulation and then applied to a physical robot [10,11]. Although these approaches are promising, they incur an intensive cost for learning each single task, which is not desirable for physical robots, or require a precise simulation model, which is not always available. Instead of learning controllers for specific tasks, this study focuses on learning the vision–motor coordination of manipulators to provide a general prior for the vision-based control of grasping and manipulation.