Showing papers on "Active learning (machine learning) published in 1978"

Learning by Example

Abstract: Everyone has many personal experiences of learning by example. While much psychological research has investigated “concept learning” (cf. Bruner, Goodnow, & Austin, 1956; Hayes-Roth & Hayes-Roth, in press; Hunt, 1952), that rubric is too narrow to embrace the variety of situations in which learning by example occurs. A brief list of such situations includes: 1. Traditional concept learning, such as inducing the class characteristics of “triangle”: “Three distinct line segments such that each line segment is coterminous, with a different line segment at each of its endpoints.” Such a rule can be induced from various examples of triangles; all examples necessarily manifest the rule, although they may differ from one another in irrelevant ways (e.g., in absolute and relative size, shape, orientation, color, texture). Note that although most traditional concept learning tasks employ only attribute-value descriptions, this learning task requires higher order, relational logic to characterize the structural constraints among the lines of a triangle. 2. Serial pattern learning, such as predicting the next item in a conceptually organized sequence. Traditional research on this problem has centered on mathematical sequences of symbols and various algorithmic models of memory processes for simulating the sequence generator. Other examples of this type of behavior include anticipation of expectable events (e.g., words or topics in a text that are predictable from preceding context) and prediction of cyclic phenomena. In these situation, subsequences of the preceding sequence of items serve as examples from which the sequence generation rule is induced.

On a Learning System

Abstract: In this paper a computer model is proposed, a learning system is described which acquires information from its environment by a learning process and organizes it into a hierarchical structure. The paper describes the organization of the knowledge structures involved and gives the algorithms which perform the learning and organizational tasks.

Design of stochastic learning automata using adaptive digital logic elements

Abstract: The hardware design of stochastic learning automata using adaptive digital logic elements is considered. Such techniques, based on digital stochastic computing, are shown to provide economical and fast learning-time computations. Experimental results are presented for a variety of linear learning algorithms.

An adaptive learning control system for aircraft

Abstract: A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions