PL EN


2013 | 152 | 42-58
Article title

Analiza dyskryminacyjna - stan aktualny i kierunki rozwoju

Content
Title variants
EN
Discriminant Analysis - State of the Art and Future Developments
Languages of publication
PL
Abstracts
EN
The aim of the discriminant analysis is to partition the multivariate feature space into subspaces in order to separate observations belonging to different classes. In other words, its task is to find a model that can give class descriptions on the basis of a set containing previously classified observations. Then the model is applied to classify new ones with a minimum error. Founded in 1936 by Fisher, the discriminant analysis had become an important part of multivariate statistical analysis. It has many applications and is an obligatory procedure in many available data mining systems. In Poland prof. Józef Kolonko has been one of the pioneering statisticians interested in discriminant analysis. He published his book on discriminant analysis in 1980, based on cybernetics. Therefore the analysis had a broader meaning, including both supervised and unsupervised classification.
Year
Volume
152
Pages
42-58
Physical description
Contributors
References
  • Breiman L., Friedman J., Olshen R., Stone C. (1984): Classification and Regression Trees. CRC Press, London.
  • Bryan J.G. (1951): The Generalized Discriminant Function: Mathematical Foundation and Computational Routine. Harvard Education Review, 21, s. 90-95.
  • Duda R. O., Hart P. E., Storck G. E. (2001): Pattern Classification. John Wiley & Sons, New York.
  • Fisher L.A. (1936): The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, t. 7, s. 179-188.
  • Friedman J. H. (1989): Regularized Discriminant Analysis. "Journal of the American Statistical Association", 84, s. 165-175.
  • Gatnar E. (1998): Symboliczne metody klasyfikacji danych. Wydawnictwo Naukowe PWN, Warszawa.
  • Gatnar E. (2001): Nieparametryczna metoda dyskryminacji i regresji. Wydawnictwo Naukowe PWN, Warszawa.
  • Gatnar E. (2008): Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji. Wydawnictwo Naukowe PWN, Warszawa.
  • Hastie T., Tibshirani R., Friedman J. (2001): The Elements of Statistical Learning. Springer Series in Statistics, Springer, Berlin.
  • Huberty G. J. (1995): Applied Discriminant Analysis. John Wiley & Sons, New York.
  • Jajuga K. (1990): Statystyczna teoria rozpoznawania obrazów. Wydawnictwo Naukowe PWN, Warszawa.
  • Jajuga K. (1993): Statystyczna analiza wielowymiarowa. Państwowe Wydawnictwo Naukowe, Warszawa.
  • Kaas G. V. (1980): An Exploratory Technique for Investigating Large Quantities of Categorical Data. "Applied Statistics", 29, s. 119-127.
  • Kolonko J. (1980): Analiza dyskryminacyjna w badaniach ekonomicznych. PWN, Warszawa.
  • McLachlan G.J. (1992): Discriminant Analysis and Statistical Pattern Recognition. John Wiley & Sons, New York.
  • Morgan J.N., Sonquist J.A. (1963): Problems in the Analysis of Survey Data: A Proposal. "Journal of the American Statistical Association", 58, s. 417-434.
  • Nilsson N.J. (1965): Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill.
  • Quinlan J.R. (1983): Learning Efficient Classification Procedures and their Application to Chess and Games. W: R. Michalski, J. Carbonell, T. Mitchell (eds.): Machine Learning. An Artificial Intelligence Approach. Tioga, Palo Alto, s. 126-142.
  • Quinlan J.R. (1986): Induction of decision trees, Machine Learning, 1, s. 81-106.
  • Quinlan J.R. (1993): C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
  • Rao C. (1948): The Utilisation of Multiple Measurements in Problems of Biological Classification. "Journal of the Royal Statistical Society B", 10, s. 159-203.
  • Ripley B.D. (1996): Pattern Recognition and Neural Networks. Cambridge University Press. Cambridge.
  • Rosenblatt F. (1958): The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. "Psychological Review", 65(6), s. 386-408.
  • Titterington D.M., Murray G.D., Murray L.S., Spiegelhalter D.J., Skene A.M., Habbema J.D., Gelpke G.J. (1981): Comparison of Discriminant Techniques Applied to Complex Data Sets of Head Injured Patients. "Journal of the Royal Statistical Society, Series A", 144, s. 145-175.
  • Vapnik V. (1995): The Nature of Statistical Learning Theory. Springer, Berlin.
  • Vapnik V. (1998): Statistical Learning Theory. John Wiley and Sons, New York.
  • Wernecke K.-D. (1992): A Coupling Procedure for Discrimination of Mixed Data. "Biometrics", 48, s. 497-506.
Document Type
Publication order reference
Identifiers
ISSN
2083-8611
YADDA identifier
bwmeta1.element.desklight-776818cc-7f2f-44b7-a6d7-ade2a44f7573
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.