Neural networks applied to data analysis

[1]  Robert P. W. Duin,et al.  Stabilizing classifiers for very small sample sizes , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[2]  Robert P. W. Duin,et al.  On the nonlinearity of pattern classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[3]  Yoshihiko Hamamoto,et al.  Evaluation of an anti-regularization technique in neural networks , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[4]  Josef Kittler,et al.  Combining classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Robert P. W. Duin,et al.  Estimating the Reliability of Neural Network Classifications , 1996, ICANN.

[6]  Kagan Tumer,et al.  Analysis of decision boundaries in linearly combined neural classifiers , 1996, Pattern Recognit..

[7]  Anil K. Jain,et al.  Artificial neural networks for feature extraction and multivariate data projection , 1995, IEEE Trans. Neural Networks.

[8]  Anil K. Jain,et al.  A nonlinear projection method based on Kohonen's topology preserving maps , 1992, IEEE Trans. Neural Networks.

[9]  R. Duin.  Small sample size generalization , 1995 .

[10]  Robert P. W. Duin,et al.  The effective capacity of multilayer feedforward network classifiers , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[11]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[12]  Roberto Battiti,et al.  Democracy in neural nets: Voting schemes for classification , 1994, Neural Networks.

[13]  Martin Anthony,et al.  On The Power of Linearly Weighted Neural Networks , 1993 .

[14]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[15]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[16]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[17]  Yann LeCun,et al.  Transforming Neural-Net Output Levels to Probability Distributions , 1990, NIPS.

[18]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[19]  John S. Baras,et al.  Convergence of Kohonen's learning vector quantization , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[20]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[21]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[22]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[23]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[24]  K. Schulten,et al.  Kohonen's self-organizing maps: exploring their computational capabilities , 1988, IEEE 1988 International Conference on Neural Networks.

[25]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[26]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[27]  Gautam Biswas,et al.  Evaluation of Projection Algorithms , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[29]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[30]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .