On Suitable Conditions For Statistical Pattern Recognition Without Supervision

Previous article Next article On Suitable Conditions for Statistical Pattern Recognition without SupervisionDavid B. Cooper and Ralph J. SchwarzDavid B. Cooper and Ralph J. Schwarzhttps://doi.org/10.1137/0117077PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout[1] G. H. Ball, Data analysis in the social sciences: What about the details?, Proc. Fall Joint Computer Conference, Las Vegas, Nevada, Spartan Books, Washington, District of Columbia, 1965, 533–559 Google Scholar[2] N. Alens, Compound Bayes learning without a teacher, Tech. Rep., 6151-2, Stanford Electronics Laboratory, Stanford University, Stanford, 1967 Google Scholar[3] David B. Cooper and , Paul W. Cooper, Nonsupervised adaptive signal detection and pattern recognition, Information and Control, 7 (1964), 416–444 10.1016/S0019-9958(64)90502-9 MR0169721 0199.22402 CrossrefGoogle Scholar[4] D. B. Cooper, Masters Thesis, On the existence of nonsupervised adaptive signal detectors; and detector estimation using stochastic approximation methods, Doctoral thesis, Columbia University, New York, 1966 Google Scholar[5] G. Nagy, State of the art in pattern recognition, Proc. IEEE., 56 (1968), 836–862 CrossrefISIGoogle Scholar[6] David B. Cooper and , Paul W. Cooper, Adaptive pattern recognition and signal detection without supervision., IEEE Internat. Convention Record, 1964 (1964), 246–256, New York MR0232492 Google Scholar[7] Henry Teicher, Identifiability of finite mixtures, Ann. Math. Statist., 34 (1963), 1265–1269 MR0155376 0137.12704 CrossrefISIGoogle Scholar[8] Henry Teicher, Identifiability of mixtures, Ann. Math. Statist., 32 (1961), 244–248 MR0120677 0146.39302 CrossrefGoogle Scholar[9] Herbert Robbins, The empirical Bayes approach to statistical decision problems, Ann. Math. Statist., 35 (1964), 1–20 MR0163407 0138.12304 CrossrefISIGoogle Scholar[10] Pál Medgyessy, Decomposition of superpositions of distribution functions, Akadémiai Kiadó, Budapest, 1961, 227– MR0121823 0094.32801 Google Scholar[11] Marek Fisz, Probability theory and mathematical statistics, Third edition. Authorized translation from the Polish. Translated by R. Bartoszynski, John Wiley & Sons Inc., New York, 1963xvi+677 MR0164358 0123.34504 Google Scholar[12] Michel Loève, Probability theory, 2nd ed. The University Series in Higher Mathematics. D. Van Nostrand Co., Inc., Princeton, N. J.-Toronto-New York-London, 1960xvi+685 MR0123342 0095.12201 Google Scholar[13] J. R. Blum, On the convergence of empiric distribution functions, Ann. Math. Statist., 26 (1955), 527–529 MR0070871 0065.11303 CrossrefISIGoogle Scholar[14] Emanuel Parzen, On estimation of a probability density function and mode, Ann. Math. Statist., 33 (1962), 1065–1076 MR0143282 0116.11302 CrossrefISIGoogle Scholar[15] M. V. Johns, Herbert Solomon, An empirical Bayes approach to non-parametric two-way classification.Studies in item analysis and prediction, Stanford Univ. Press, Stanford, Calif., 1961, 221–232 MR0121921 0106.13001 Google Scholar Previous article Next article FiguresRelatedReferencesCited ByDetails Partitioning mixed pbobability distributions into their constituentsJournal of Soviet Mathematics, Vol. 7, No. 2 | 1 Feb 1977 Cross Ref When should a learning machine ask for help?IEEE Transactions on Information Theory, Vol. 20, No. 4 | 1 Jul 1974 Cross Ref Volume 17, Issue 5| 1969SIAM Journal on Applied Mathematics835-1015 History Submitted:23 February 1968Published online:12 July 2006 InformationCopyright © 1969 Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/0117077Article page range:pp. 872-896ISSN (print):0036-1399ISSN (online):1095-712XPublisher:Society for Industrial and Applied Mathematics