Results 1  10
of
14
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)
 Add to MetaCart
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.
Locally Weighted Learning for Control
, 1996
"... Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We ex ..."
Abstract

Cited by 197 (19 self)
 Add to MetaCart
Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
Efficient Memorybased Learning for Robot Control
, 1990
"... This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensorsan approach which is formalized here as the $AB (StateActionBehaviour) ..."
Abstract

Cited by 121 (3 self)
 Add to MetaCart
This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensorsan approach which is formalized here as the $AB (StateActionBehaviour) control cycle. A method of learning is presented in which all the experiences in the lifetime of the robot are explicitly remembered. The experiences are stored in a manner which permits fast recall of the closest previous experience to any new situation, thus permitting very quick predictions of the effects of proposed actions and, given a goal behaviour, permitting fast generation of a candidate action. The learning can take place in highdimensional nonlinear control spaces with realvalued ranges of variables. Furthermore, the method avoids a number of shortcomings of earlier learning methods in which the controller can become trapped in inadequate performance which does not improve. Also considered is how the system is made resistant to noisy inputs and how it adapts to environmental changes. A well founded mechanism for choosing actions is introduced which solves the experiment/perform dilemma for this domain with adequate computational efficiency, and with fast convergence to the goal behaviour. The dissertation explefins in detail how the $AB control cycle can be integrated into both low and high complexity tasks. The methods and algorithms are evaluated with numerous experiments using both real and simulated robot domefins. The final experiment also illustrates how a compound learning task can be structured into a hierarchy of simple learning tasks.
Assessing the quality of learned local models
 Advances in Neural Information Processing Systems 6
, 1994
"... An approach is presented to learning high dimensional functions in the case where the learning algorithm can affect the generation of new data. A local modeling algorithm, locally weighted regression, is used to represent the learned function. Architectural parameters of the approach, such as distan ..."
Abstract

Cited by 47 (16 self)
 Add to MetaCart
An approach is presented to learning high dimensional functions in the case where the learning algorithm can affect the generation of new data. A local modeling algorithm, locally weighted regression, is used to represent the learned function. Architectural parameters of the approach, such as distance metrics, are also localized and become a function of the query point instead of being global. Statistical tests are given for when a local model is good enough and sampling should be moved to a new area. Our methods explicitly deal with the case where prediction accuracy requirements exist during exploration: By gradually shifting a “center of exploration ” and controlling the speed of the shift with local prediction accuracy, a goaldirected exploration of state space takes place along the fringes of the current data support until the task goal is achieved. We illustrate this approach with simulation results and results from a real robot learning a complex juggling task. 1
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 ..."
Abstract

Cited by 31 (8 self)
 Add to MetaCart
(Show Context)
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...
The Dynamic Pattern Selection Algorithm: Effective Training and Controlled Generalization of Backpropagation Neural Networks
, 1994
"... In the following report the problem of selecting proper training sets for neural network time series prediction or function approximation is addressed. As a result of analyzing the relation between approximation and generalization, a new measure, the generalization factor is introduced. Using this f ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
In the following report the problem of selecting proper training sets for neural network time series prediction or function approximation is addressed. As a result of analyzing the relation between approximation and generalization, a new measure, the generalization factor is introduced. Using this factor and cross validation a new algorithm, the dynamic pattern selection, is developed. Dynamically selecting the training patterns during training establishes the possibility of controlling the generalization properties of the neural net. As a consequence of the proposed selection criterion, the generalization error is limited to the training error. As an additional beneøt, the practical problem of selecting a concise training set out of known data is likewise solved. By employing two time series prediction tasks, the results for dynamic pattern selection training and for øxed training sets are compared. The favorable properties of the dynamic pattern selection, namely lower computational ...
Estimating Lyapunov Exponents with Nonparametric Regression
, 1990
"... We discuss procedures based on nonparametric regression for estimating the dominant Lyapunov exponent Al from timeseries data generated by a system x t ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
We discuss procedures based on nonparametric regression for estimating the dominant Lyapunov exponent Al from timeseries data generated by a system x t
Nonparametric Regression for Learning Nonlinear Transformations
 PRERATIONAL INTELLIGENCE IN STRATEGIES, HIGHLEVEL PROCESSES AND COLLECTIVE BEHAVIOR
"... Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than preprogrammed by evolution. Such selforganizing processes, capable of discovering nonlinear dependencies between different groups of signals, are one essential part of prerational intelligence. While neural network algorithms seem to be the natural choice when searching for solutions for learning transformations, this paper will take a more careful look at which types of neural networks are actually suited for the requirements of an autonomous learning system. The approach that we will pursue is guided by recent developments in learning theory that have linked neural network learning to well established statistical theories. In particular, this new statistical understanding has given rise to the development of neural network systems that are directly based on statistical methods. One family of such methods stems from nonparametric regression. This paper will compare nonparametric learning with the more widely used parametric counterparts in a non technical fashion, and investigate how these two families differ in their properties and their applicabilities. We will argue that nonparametric neural networks offer a set of characteristics that make them a very promising candidate for online learning in autonomous system.
Nonparametric regression for learning
 CENTER FOR INTERDISCIPLINARY RESEARCH, UNIVERSITY OF BIELEFELD
, 1994
"... In recent years, learning theory has been increasingly influenced by the fact that many learning algorithms have at least in part a comprehensive interpretation in terms of well established statistical theories. Furthermore, with little modification, several statistical methods can be directly cast ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
In recent years, learning theory has been increasingly influenced by the fact that many learning algorithms have at least in part a comprehensive interpretation in terms of well established statistical theories. Furthermore, with little modification, several statistical methods can be directly cast into learning algorithms. One family of such methods stems from nonparametric regression. This paper compares nonparametric learning with the more widely used parametric counterparts and investigates how these two families differ in their properties and their applicability.
Control Systems Magazine, 14, 1, pp.5771. Robot Juggling: An Implementation of Memorybased Learning
"... This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memorybased local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given ..."
Abstract
 Add to MetaCart
(Show Context)
This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memorybased local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast realtime learning of the task within 40 to 100 trials.