Papperoni

1995

Comparison of Learning Algorithms for Handwritten Digit Recognition

V. Vapnik

citations

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AI summary

This paper compares the performance of several classifiers on a handwritten digit database, considering accuracy, rejection, training time, recognition time, and memory usage, finding that boosted LeNet 4 achieves 0.7% test error.

Main Contributions

Compared various classification algorithms for handwritten digit recognition.
Evaluated algorithms based on accuracy, rejection rate, training time, recognition time, and memory requirements.
Introduced a database of handwritten digits for benchmarking.
Achieved a test error rate of 0.7% using a Boosted LeNet 4, the best performance among the tested classifiers.

Abstract

This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.

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References [11]

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Yann Lecun, B. Boser, John S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel - 1990

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Just a minor derivation of the previous paper, adding more layers to the net. Pretty much the same exact text than the other one. Uninteresting if you read the other.

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SVMs?

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on June 21, 2025

Good paper that gives an overview of what was SotA on digit recognition in the mid-90s. It's not so well written but it's easy to understand. I think that this is even better than LeCun's other paper to understand what's going on