ACM Home Page
Please provide us with feedback. Feedback
Nonlinear Regression With Linear Constraints: An Extension of the Magnified Diagonal Method
Full text PdfPdf (373 KB)
Source Journal of the ACM (JACM) archive
Volume 17 ,  Issue 3  (July 1970) table of contents
Pages: 446 - 452  
Year of Publication: 1970
ISSN:0004-5411
Author
Richard I. Shrager  Department of Health, Education and Welfare, Division of Computer Research and Technology, National Institutes of Health, Bethesda, Maryland
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 53,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   peer to peer  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/321592.321597
What is a DOI?

ABSTRACT

A syntax-directed picture analysis system based on a formal picture description scheme is described. The system accepts a description of a set of pictures in terms of a grammar generating strings in a picture description language; the grammar is explicitly used to direct the analysis or parse, and to control the calls on pattern classification routines for primitive picture components. Pictures are represented by directed graphs with labeled edges, where the edges denote elementary picture components and the graph connectivity mirrors the picture component connectivity; blank and don't care “patterns” allow the description of simple relations between visible patterns. The bulk of the paper is concerned with the picture parsing algorithm which is an n-dimensional analog of a classical top-down string parser, and an application of an implemented system to the analysis of spark chamber film. The potential benefits of this approach, as demonstrated by the application, include ease of implementation and modification of picture processing systems, and simplification of the pattern recognition problem by automatically taking advantage of contextual information.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
LEVENBERG, K. A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2, 2 (July 1944), 164---168.
 
3
MARQUARDT, D.W. An algorithm for least-squares determination of non-linear parammeters. SIAM J. Appl. Math. 11, 2 (June 1963), 431-441.
 
4
MEETER, D.A. On a theorem used in non-linear least squares. SIAM J. Appl. Math. 1~ (Sept. 1966), 1176-1179.
 
5
1VIORRISON, D. D. Methods for non-linear least squares problems and convergence proofs. Tracking Programs and Orbit Determination, Proc. Jet Propulsion Laboratory Seminar, 1960, pp. 1-9. (Available from Space Technology Lab., Redondo Beach, Calif.) B. Quadratic Programming
 
6
HADLEY, G. Nonlinear and Dynamic Programming. Addison-Wesley, Reading, Mass., 1964.
 
7
HILDRETH, C. A quadratic programming procedure. Nay. Res. Logistics Quart. 14 (1957), 79-85.
 
8
HOUTHAKKER, H.S. The capacity method of quadratic programming. Econometrica ~8 (1960), 62-87.
 
9
MARKOWITZ, H. Portfolio Selection (Cowles Foundation Monograph No. 16). Wiley, New York, 1959.
 
10
WOLFE, P. The simplex method for quadratic programming. Econometrica ~7 (1959), 382--398.
 
11
SHRAGER, R. A quadratic programming algorithm. (Submitted for publication.)



Peer to Peer - Readers of this Article have also read: