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Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract

Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
MemoryBased Neural Networks For Robot Learning
 Neurocomputing
, 1995
"... This paper explores a memorybased approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their ..."
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Cited by 31 (8 self)
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This paper explores a memorybased approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memorybased, robot learning, locally weighted regression, nearest neighbor, local models. 1 Introduction An important problem in motor learning is approxim...
An Investigation of the Gradient Descent Process in Neural Networks
, 1996
"... Usually gradient descent is merely a way to find a minimum, abandoned if a more efficient technique is available. Here we investigate the detailed properties of the gradient descent process, and the related topics of how gradients can be computed, what the limitations on gradient descent are, and ..."
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Usually gradient descent is merely a way to find a minimum, abandoned if a more efficient technique is available. Here we investigate the detailed properties of the gradient descent process, and the related topics of how gradients can be computed, what the limitations on gradient descent are, and how the secondorder information that governs the dynamics of gradient descent can be probed. To develop our intuitions, gradient descent is applied to a simple robot arm dynamics compensation problem, using backpropagation on a temporal windows architecture. The results suggest that smooth filters can be easily learned, but that the deterministic gradient descent process can be slow and can exhibit oscillations. Algorithms to compute the gradient of recurrent networks are then surveyed in a general framework, leading to some unifications, a deeper understanding of recurrent networks, and some algorithmic extensions. By regarding deterministic gradient descent as a dynamic system we obtain results concerning its convergence, and a quantitative theory of its behavior