More Web Proxy on the site http://driver.im/

Article

Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'

Authors:

Daniel Keysers,

Hermann NeyAuthors Info & Claims

MCS '01: Proceedings of the Second International Workshop on Multiple Classifier Systems

Pages 109 - 118

Published: 02 July 2001 Publication History

Abstract

In this paper, we present a combined classification approach called the 'virtual test sample method'. Contrary to classifier combination, where the outputs of a number of classifiers are used to come to a combined decision for a given observation, we use multiple instances generated from the original observation and a single classifier to compute a combined decision. In our experiments, the virtual test sample method is used to improve the performance of a statistical classifier based on Gaussian mixture densities. We show that this approach has some desirable theoretical properties and performs very well, especially when combined with the use of invariant distance measures. In the experiments conducted throughout this work, we obtained an excellent error rate of 2.2% on the original US Postal Service task.

References

[1]

Breiman, L.: Bagging Predictors. Technical Report 421, Department of Statistics, University of California at Berkeley, 1994.

[2]

Dahmen, J., Keysers, D., Ney, H., Güld, M.: Statistical Image Object Recognition using Mixture Densities. Journal of Mathematical Imaging and Vision, Vol. 14, No. 3, Kluwer Academic Publishers, pp. 285-296, May 2001.

[3]

Dempster A., Laird, N., Rubin, D.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, 39(B), pp. 1-38, 1977.

[4]

Drucker, H., Schapire, R., Simard, P.: Boosting Performance in Neural Networks. Int. Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 4, pp. 705-719, 1993.

[5]

Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons, 1973.

[6]

Freund, Y., Schapire, R.: Experiments with a New Boosting Algorithm. 13th Int. Conference on Machine Learning, Bari, Italy, pp. 148-156, July 1996.

[7]

Keysers, D., Dahmen, J., Ney, H.: A Probabilistic View on Tangent Distance. 22nd Symposium German Association for Pattern Recognition (DAGM), Kiel, Germany, pp. 107-114, 2000.

[8]

Keysers, D., Dahmen, J., Theiner, T., Ney, H.: Experiments with an Extended Tangent Distance. 15th Int. Conference on Pattern Recognition, Barcelona, Spain, Vol. 2, pp. 38-42, September 2000.

[9]

Kittler, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 3, pp. 226-239, March 1998.

[10]

Ney, H.: On the Probabilistic Interpretation of Neural Network Classifiers and Discriminative Training Criteria, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, pp. 107-119, February 1995.

[11]

Schölkopf, B.: Support Vector Learning. Oldenbourg Verlag, Munich, 1997.

[12]

Schölkopf, B., Simard, P., Smola, A., Vapnik, V.: Prior Knowledge in Support Vector Kernels. Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA, pp. 640-646, 1998.

[13]

Schapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the Margin: A New Explanation for the E_ectiveness of Voting Methods. The Annals of Statistics, Vol. 26, No. 5, pp. 1651-1686, 1998.

[14]

Simard, P., Le Cun, Y., Denker, J.: E_cient Pattern Recognition using a New Transformation Distance. Advances in Neural Information Processing Systems 5, Morgan Kaufmann, San Mateo CA, pp. 50-58, 1993.

[15]

Simard, P., Le Cun, Y., Denker, J., Victorri, B.: Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation. Lecture Notes in Computer Science, Vol. 1524, Springer, pp. 239-274, 1998.

[16]

Vapnik, V.: The Nature of Statistical Learning Theory, Springer, New York, pp. 142-143, 1995.

Cited By

Geurts PWehenkel LDzeroski SDe Raedt LWrobel S(2005)Closed-form dual perturb and combine for tree-based modelsProceedings of the 22nd international conference on Machine learning10.1145/1102351.1102381(233-240)Online publication date: 7-Aug-2005
https://dl.acm.org/doi/10.1145/1102351.1102381

Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Recommendations

A Combined Classification Algorithm Based on C4.5 and NB
ISICA '08: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence

When our learning task is to build a model with accurate classification, C4.5 and NB are two very important algorithms for achieving this task because of their simplicity and high performance. In this paper, we present a combined classification ...
Handwritten Kanji Recognition Using Combined Complementary Classifiers in a Cascade Arrangement
ICDAR '99: Proceedings of the Fifth International Conference on Document Analysis and Recognition

In classifier combination, the degree of recognition accuracy improvement depends not only on how classifiers are combined, but also on how much they complement each other. From this viewpoint, a complementary classifier that is combined with an ...
Word segmentation of handwritten text using supervised classification techniques
Abstract
Recent work on extracting features of gaps in handwritten text allows a classification of these gaps into inter-word and intra-word classes using suitable classification techniques. In this paper, we first analyse the features of the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

MCS '01: Proceedings of the Second International Workshop on Multiple Classifier Systems

July 2001

454 pages

ISBN:3540422846

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 02 July 2001

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Geurts PWehenkel LDzeroski SDe Raedt LWrobel S(2005)Closed-form dual perturb and combine for tree-based modelsProceedings of the 22nd international conference on Machine learning10.1145/1102351.1102381(233-240)Online publication date: 7-Aug-2005
https://dl.acm.org/doi/10.1145/1102351.1102381

View Options

View options

Media

Figures

Other

Tables

View Table of Contents