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147
Learning to predict by the methods of temporal differences
 MACHINE LEARNING
, 1988
"... This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional predictionlearning methods assign credit by means of the difference between predi ..."
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Cited by 1226 (45 self)
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This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional predictionlearning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporaldifference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervisedlearning methods. For most realworld prediction problems, temporaldifference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporaldifference methods can be applied to advantage.
ANFIS: AdaptiveNetworkBased Fuzzy Inference System
, 1993
"... This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping bas ..."
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Cited by 432 (5 self)
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This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping based on both human knowledge (in the form of fuzzy ifthen rules) and stipulated inputoutput data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components onlinely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. 1 Introduction System modeling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with illdefine...
Exponentiated Gradient Versus Gradient Descent for Linear Predictors
 Information and Computation
, 1995
"... this paper, we concentrate on linear predictors . To any vector u 2 R ..."
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Cited by 247 (12 self)
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this paper, we concentrate on linear predictors . To any vector u 2 R
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
 J. Neurosci
, 1996
"... We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fl ..."
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Cited by 245 (29 self)
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We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcottical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decisionmaking task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner. Key words: prediction; dopamine; diffuse ascending systems; synaptic plasticity; reinforcement learning; reward In mammals, mesencephalic dopamine neurons participate in a number of important cognitive and physiological functions including motivational processes (Wise, 1982; Fibiger and Phillips, 1986; Koob and Bloom, 1988) reward processing (Wise, 1982) working
Learning and Sequential Decision Making
 LEARNING AND COMPUTATIONAL NEUROSCIENCE
, 1989
"... In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been proposed as models of ..."
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Cited by 195 (10 self)
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In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been proposed as models of animal learning in classical conditioning experiments. Here we relate TD methods to decision tasks formulated in terms of a stochastic dynamical system whose behavior unfolds over time under the influence of a decision maker's actions. Strategies are sought for selecting actions so as to maximize a measure of longterm payoff gain. Mathematically, tasks such as this can be formulated as Markovian decision problems, and numerous methods have been proposed for learning how to solve such problems. We show how a TD method can be understood as a novel synthesis of concepts from the theory of stochastic dynamic programming, which comprises the standard method for solving such tasks when a model of the dynamical system is available, and the theory of parameter estimation, which provides the appropriate context for studying learning rules in the form of equations for updating associative strengths in behavioral models, or connection weights in connectionist networks. Because this report is oriented primarily toward the nonengineer interested in animal learning, it presents tutorials on stochastic sequential decision tasks, stochastic dynamic programming, and parameter estimation.
The Helmholtz Machine
, 1995
"... Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative model ..."
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Cited by 194 (22 self)
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Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical selfsupervised learning that may relate to the function of bottomup and topdown cortical processing pathways.
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 147 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
A New Class Of Incremental Gradient Methods For Least Squares Problems
 SIAM J. Optim
, 1996
"... The LMS method for linear least squares problems di#ers from the steepest descent method in that it processes data blocks onebyone, with intermediate adjustment of the parameter vector under optimization. This mode of operation often leads to faster convergence when far from the eventual limit, an ..."
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Cited by 34 (3 self)
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The LMS method for linear least squares problems di#ers from the steepest descent method in that it processes data blocks onebyone, with intermediate adjustment of the parameter vector under optimization. This mode of operation often leads to faster convergence when far from the eventual limit, and to slower (sublinear) convergence when close to the optimal solution. We embed both LMS and steepest descent, as well as other intermediate methods, within a oneparameter class of algorithms, and we propose a hybrid class of methods that combine the faster early convergence rate of LMS with the faster ultimate linear convergence rate of steepest descent. These methods are wellsuited for neural network training problems with large data sets. Furthermore, these methods allow the e#ective use of scaling based for example on diagonal or other approximations of the Hessian matrix. 1 Research supported by NSF under Grant 9300494DMI. 2 Dept. of Electrical Engineering and Computer Science, M...
Robust Indoor Location Estimation of Stationary and Mobile Users
, 2004
"... We present algorithms for estimating the location of stationary and mobile users based on heterogeneous indoor RF technologies. We propose two location algorithms, Selective Fusion Location Estimation (SELFLOC) and Region of Confidence (RoC), which can be used in conjunction with classical location ..."
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Cited by 34 (1 self)
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We present algorithms for estimating the location of stationary and mobile users based on heterogeneous indoor RF technologies. We propose two location algorithms, Selective Fusion Location Estimation (SELFLOC) and Region of Confidence (RoC), which can be used in conjunction with classical location algorithms such as triangulation, or with thirdparty commercial location estimation systems. The SELFLOC algorithm infers the user location by selectively fusing location information from multiple wireless technologies and/or multiple classical location algorithms in a theoretically optimal manner. The RoC algorithm attempts to overcome the problem of aliasing in the signal domain, where different physical locations have similar RF characteristics, which is particularly acute when users are mobile. We have empirically validated the proposed algorithms using wireless LAN and Bluetooth technology. Our experimental results show that applying SELFLOC for stationary users when using multiple wireless technologies and multiple classical location algorithms can improve location accuracy significantly, with mean distance errors as low as 1.6 m. For mobile users we find that using RoC can allow us to obtain mean errors as low as 3.7 m. Both algorithms can be used in conjunction with a commercial location estimation system and improve its accuracy further.