|
4828
|
Genetic Algorithms
– Goldberg
- 1989
|
|
2961
|
Pattern Classification and Scene Analysis
– Duda, Hart
- 1973
|
|
2140
|
Learning Internal Representations by Error Propagation
– Rumelhart, Hinton, et al.
- 1986
|
|
938
|
Learning from Delayed Rewards
– Watkins
- 1989
|
|
885
|
Learning to Predict by the Methods of Temporal Differences
– Sutton
- 1988
|
|
366
|
Beyond Regression: New Tools for Prediction and Analysis
– Werbos
- 1974
|
|
295
|
A learning algorithm for Boltzmann Machines
– Ackley, Hinton, et al.
- 1985
|
|
273
|
Connectionist learning procedures
– Hinton
- 1989
|
|
252
|
A stochastic approximation method
– Robbins, Munro
- 1951
|
|
212
|
Temporal Credit Assignment in Reinforcement Learning
– Sutton
- 1984
|
|
196
|
Forward Models: Supervised Learning with a Distal Teacher
– Jordan, Rumelhart
- 1992
|
|
177
|
Distributed representations
– Hinton, McClelland, et al.
- 1986
|
|
171
|
Unsupervised learning
– Barlow
- 1989
|
|
135
|
Neuronlike elements that can solve difficult learning control problems
– Barto, Sutton, et al.
- 1983
|
|
113
|
Learning with localized receptive fields
– Moody, Darken
- 1988
|
|
112
|
BOXES: An experiment in adaptive control
– Michie, Chambers
- 1968
|
|
105
|
Learning Automata --- an Introduction
– Narendra, Thathachar
- 1989
|
|
91
|
A hierarchical neural-network model for control and learning of voluntary movement
– Kawato, Furukawa, et al.
- 1987
|
|
91
|
Efficient Memory-based Learning for Robot Control
– Moore
- 1990
|
|
87
|
The truck backer-upper: An example of selflearning in neural networks
– Nguyen, Widrow
- 1990
|
|
85
|
Quasi-static assembly of compliantly supported rigid parts
– Whitney
- 1982
|
|
68
|
Learning to control an inverted pendulum using neural networks
– Anderson
- 1989
|
|
63
|
Learning to control an unstable system with forward modeling
– Jordan, Jacobs
- 1990
|
|
59
|
Stochastic estimation of the maximum of a regression function
– Kiefer, Wolfowitz
- 1952
|
|
55
|
Learning logic
– Parker
- 1985
|
|
53
|
A stochastic reinforcement learning algorithm for learning realvalued functions. Neural Networks 3
– Gullapalli
- 1990
|
|
52
|
Pattern–recognizing stochastic learning automata’, in
– Barto, Anandan
- 1985
|
|
49
|
An application of the principle of maximum information preservation to linear systems
– Linsker
- 1989
|
|
47
|
Connectionist learning for control: An overview
– Barto
- 1990
|
|
43
|
Training and tracking in robotics
– Selfridge, Sutton, et al.
- 1985
|
|
41
|
Weight Perturbation: An Optimal Architecture and learning Technique for Analog VLSI Feedforward and recurrent Multilayer Networks
– Jabri, Flower
- 1992
|
|
41
|
Feedback Control Systems
– Smith
- 1958
|
|
39
|
A menu of designs for reinforcement learning over time
– Werbos
- 1990
|
|
38
|
Supervised learning and systems with excess degrees of freedom
– Jordan
- 1988
|
|
37
|
Une procédure d’apprentissage pour réseau à seuil assymétrique. In Cognitiva 85: A la Frontière de l’Intelligence Artificielle des Sciences de la Connaissance des Neurosciences
– LeCun
- 1985
|
|
33
|
Computational schemes and neural network models for formation and control of multijoint arm trajectory
– Kawato
- 1990
|
|
32
|
Associative search networks: a reinforcement learning associativememory
– Barto, Sutton, et al.
- 1981
|
|
32
|
A dual back-propagation scheme for scalar reward learning
– Munro
- 1987
|
|
32
|
Evolving Controls for Unstable Systems
– Wieland
- 1990
|
|
29
|
Automaton Theory and Modeling of Biological Systems
– Tsetlin
- 1973
|
|
26
|
Gradient following without back-propagation in layered networks
– Barto, Jordan
- 1987
|
|
24
|
Using associative content-addressable memories to control robots
– Atkeson, Reinkensmeyer
- 1989
|
|
24
|
A self-learning automaton with variable resolution for high precision assembly by industrial robots
– Simons, Brussel, et al.
- 1982
|
|
22
|
Learning to control a dynamic physical system
– Connell, Utgoff
- 1987
|
|
22
|
On the use of backpropagation in associative reinforcement learning
– Williams
- 1988
|
|
20
|
Reinforcement learning in connectionist networks: A mathematical analysis
– Williams
- 1986
|
|
18
|
Reinforcement-Learning Connectionist Systems
– Williams
- 1987
|
|
17
|
Action
– Jordan, Rosenbaum
- 1988
|
|
17
|
Hierarchical Neural Network Model for Voluntary Movement with Application to Robotics
– Kawato, Uno, et al.
- 1988
|
|
16
|
Neural networks and radial basis functions in classifying static speech patterns
– Niranjan, Fallside
- 1990
|