Showing papers on "Active learning (machine learning) published in 1970"
TL;DR: The basic concept of learning control is introduced, and the following five learning schemes are briefly reviewed: 1) trainable controllers using pattern classifiers, 2) reinforcement learning control systems, 3) Bayesian estimation, 4) stochastic approximation, and 5) Stochastic automata models.
Abstract: The basic concept of learning control is introduced. The following five learning schemes are briefly reviewed: 1) trainable controllers using pattern classifiers, 2) reinforcement learning control systems, 3) Bayesian estimation, 4) stochastic approximation, and 5) stochastic automata models. Potential applications and problems for further research in learning control are outlined.
204 citations
TL;DR: This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems.
Abstract: The Bayesian learning scheme is computationally infeasible for most of the unsupervised learning problems. This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems. In this scheme a sample is probabilistically assigned with a class with appropriate probabilities computed using all the information available: Then the sample is used in learning the parameter values given this assignment of the class. The convergence of the scheme is established and a comparison with the best linear estimator is presented.
121 citations
TL;DR: It is demonstrated that identification theory implies unsupervised learning is possible in many important cases, and a general method is presented as inclusive as the one revealed here, which is effective for all the many cases wherein unsuper supervised learning is known to be possible.
Abstract: The first portion of this paper is tutorial. Beginning with a standard definition of an abstract pattern-recognition machine, "learning" is given a mathematical meaning and the distinction is made between supervised and unsupervised learning. The bibliography will help the interested reader retrace the history of learning in pattern recognition. The exposition now focuses attention on unsupervised learning. Carefully, it is explained how problems in this subject can be viewed as problems in the identification of finite mixtures, a statistical theory that has achieved some maturity. From this vantage point, it is demonstrated that identification theory implies unsupervised learning is possible in many important cases. The remaining sections present a general method for achieving unsupervised learning. Other authors have proposed schemes having greater computational convenience, but no method previously published is as inclusive as the one revealed here, which we demonstrate to be effective for all the many cases wherein unsupervised learning is known to be possible.
54 citations
TL;DR: Simple mathematical expressions are derived for the improvement in supervised learning provided by additional nonsupervised learning when the number of learning samples is large so that asymptotic approximations are appropriate.
Abstract: This paper treats an aspect of the learning or estimation phase of statistical pattern recognition (and adaptive statistical decision making in general). Simple mathematical expressions are derived for the improvement in supervised learning provided by additional nonsupervised learning when the number of learning samples is large so that asymptotic approximations are appropriate. The paper consists largely of the examination of a specific example, but, as is briefly discussed, the same procedure can be applied to other parametric problems and generalization to nonparametric problems seems possible. The example treated has the additional interesting aspect that the data does not have structure that would enable the machine to learn in the nonsupervised mode alone; but the additional nonsupervised learning can provide substantial improvement over the results obtainable by supervised learning alone. A second purpose of the paper is to suggest that a new fruitful area of research is the analytical study of the possible benefits of combining supervised and nonsupervised learning.
35 citations
TL;DR: A computer method of two-stage learning is employed in which the first stage is coarse and attempts to satisfy the terminal boundary conditions on the basis of subgoal learning, which yields an approximation to the optimum control law.
Abstract: Learning heuristics for an on-line controller are presented, and various aspects of the problem are discussed. The controller is required to achieve optimal regulator control for an unknown process in the face of random disturbances. A computer method of two-stage learning is employed in which the first stage is coarse and attempts to satisfy the terminal boundary conditions on the basis of subgoal learning. This yields an approximation to the optimum control law. Rote learning is also carried out during this time. The second, or tuning stage, improves on this result by a technique of reinforcement learning applied to the integral performance criterion. The effect of varying the parameters associated with the learning algorithm is studied. A discussion of a hybrid computer simulation of a second-order plant subject to one input with two possible levels is presented.
10 citations
TL;DR: A computerized adaptive learning model is presented which updates current decisions based on decisions made and experiences realized in the past and is adaptable to other learning environments in addition to that given in this paper.
Abstract: This paper presents the results of a computerized adaptive learning model. The learner in this case is a hypothetical marketing manager competing with two competitors in a common market. The model is free of heuristic rules-of-thumb and relies instead on a search procedure which updates current decisions based on decisions made and experiences realized in the past. It is adaptable to other learning environments in addition to that given in this paper.
2 citations
TL;DR: Algorithm for implementing learning controller based on subgoal concept applicable to linear stationary system and its implications for learning controller design and simulation are revealed.
Abstract: Algorithm for implementing learning controller based on subgoal concept applicable to linear stationary system
1 citations