Pattern Recognition


Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition

Classics

In Fogelman Souli´e, F. and H´erault, J. (Eds.), Neurocomputing: Algorithms, Architectures and Applications. Springer-Verlag.



QLISP: A language for the interactive development of complex systems

Classics

This paper presents a functional overview of the features and capabilities of QLISP, one of the newest of the current generation of very high level languages developed for use in Artificial Intelligence (AI) research.QLISP is both a programming language and an interactive programming environment. It embeds an extended version of QA4, an earlier AI language, in INTERLISP, a widely available version of LISP with a variety of sophisticated programming aids.The language features provided by QLISP include a variety of useful data types, an associative data base for the storage and retrieval of expressions, the ability to associate property lists with arbitrary expressions, a powerful pattern matcher based on a unification algorithm, pattern-directed function invocation, "teams" of pattern invoked functions, a sophisticated mechanism for breaking a data base into contexts, generators for associative data retrieval, and easy extensibility.System features available in QLISP include a very smooth interaction with the underlying INTERLISP language, a facility for aggregating multiple pattern matches, and features for interactive control of programs.A number of applications to which QLISP has been put are briefly discussed, and some directions for future development are presented.SRI Tech.Note 120, AI Center, SRI International, Inc., Menlo Park, Calif.



Pattern Recognition

Classics

Proceedings of the IEEE Workshop on Pattern Recognition, held at Dorado, Puerto Rico, Washington, DC: Thompson Book Co




Proving Theorems by Pattern Recognition

Classics

Communications of the ACM, Vol 4, No. 3, pp. 229-243, 1960



Pattern Recognition and Reading by Machine

Classics

"MANY EFFORTS have been made to discriminate, categorize, and quantitate patterns, and to reduce them into a usable machine language. The results have ordinarily been methods or devices with a high degree of specificity. For example, some devices require a special type font; others can read only one type font; still others require magnetic ink. We have an interest in decision-making circuits with the following qualities: (1) measurable high reliability in decision making, (2) either a high or a low reliability input, and (3) possibly low reliability components. The high specificity of the devices and methods mentioned above was felt to be a drawback for our purposes. All of these approaches prove upon inspection to center upon analysis of the specific characteristics of patterns into parts, followed by a synthesis of the whole from the parts. In these studies, pattern recognition of the whole, that is, Gestalt recognition, was chosen as a more fruitful avenue of approach and as a satisfactory problem for the initial phases of the over-all study."Proceedings of the Eastern Joint Computer Conference, pp. 225-232, New York: Association for Computing Machinery