LTF-C: Architecture, Training Algorithm and Applications of New Neural Classifier

• Autorzy: Wojnarski M.
• Języki publikacji: EN

Abstrakty

EN

This paper presents a new model of an artificial neural network solving classification problems, called Local Transfer Function Classifier (). Its architecture is very similar to this of the Radial Basis Function neural network (RBF), however it utilizes an entirely different learning algorithm. This algorithm is composed of four main parts: changing positions of reception fields, changing their sizes, insertion of new hidden neurons and removal of unnecessary ones during the training. The paper presents also results of application to three real-life tasks: handwritten digit recognition, credit approval and cancer diagnosis. was able to solve each of these problems with better accuracy than most popular classification systems. Moreover, was relatively small and fast.

Słowa kluczowe

EN

neural network; classification; recognition; radial basis function (RBF)

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2003
• Tom: Vol. 54, nr 1
• Strony: 89--105
• Opis fizyczny: bibliogr. 11 poz.

Twórcy

autor

Wojnarski M.

• Faculty of Mathematics, Informatics and Mechanics, Warsaw University, ul. Banacha 2, 02-097 Warszawa, Poland,

Bibliografia

• [1] Cover, T. M.: Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition, IEEE Trans. Elect. Comp., 14, 1965, 326-334.
• [2] Duch, W.: Datasets used for classification: comparison of results, Online: http://www.phys.uni.torun.pl/kmk/projects/datasets.html.
• [3] Duch, W., Jankowski, N.: Survey of neural transfer functions, 1999.
• [4] Fiesler, E., Beale, R., Eds.: Handbook of Neural Computation, The Computational Intelligence Library, Oxford University Press, New York, 1997.
• [5] Hebb, D. O.: The Organization of Behaviour, Wiley, New York, 1949.
• [6] Le Cun, Y.: The MNIST database of handwritten digits, Online: http://yann.lecun.com/exdb/mnist/index.html.
• [7] Le Cun, Y., et al.: Comparison of learning algorithms for handwritten digit recognition, 1995.
• [8] Mertz, C. J., Murphy, P. M.: UCI repository, Online: http://www.ics.uci.edu/pub/machine-learning-databases.
• [9] Michie, D., Spiegelhalter, D. J., Taylor, C. C.: Machine Learning, Neural and Statistical Classification, Elis Horwood, London, 1994.
• [10] Ripley, B. D.: Pattern recognition and neural networks, Cambridge University Press, Cambridge, 1996.
• [11] Wojnarski, M.: LTF-Cimulator, Online: http://rainbow.mimuw.edu.pl/∼mwojnar/ltfcim