Answers.Body: <p>To quote the relevant <a href="https://en.wikipedia.org/wiki/MNIST_database" rel="nofollow noreferrer">Wikipedia article</a>:</p>
  <blockquote>
  <p>The original creators of the database keep a list of some of the methods tested on it.[7] In their original paper, they use a support-vector machine to get an error rate of 0.8%</p>
  </blockquote>
  <p>Feel free to look up that original paper, but to me the quote strongly suggests that the first record holder was a support vector machine.</p>
  <hr />
  <h3>Edit</h3>
  <p>As liori points out the quote is misleading: In the original paper Yann LeCun et al. actually tried a slew of methods and one version of ConvNet scored best (0.7).</p>
  <p>But according to the <a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow noreferrer">MNIST-webpage</a>, after that initial paper, DeCoste and Scholkopf, MLJ 2002, reached an error of 0.56 with a SVM. So if that webpage is complete, the claim is still false.</p>
  
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Answers.CreationDate: 2022-04-13T12:32:31.103Z
Answers.Text: I don't understand how this answers the original question, which was supposedly asking if any other technique surpassed NNs on the MNIST task, not if before NNs there was a technique that performed better than them, as the OP wrote "**since** the publication of the MNIST dataset, it has never happened".
Answers.UserDisplayName: nbro
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Answers.CreationDate: 2016-12-16T23:08:00.087Z
Answers.Text: Seems like the paper referenced in [5] (http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf) on Wikipedia states both the V-SVM result of 0.8 and the boosted LeNet-4 result of 0.7, so… not in this case.
Answers.UserDisplayName: liori
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Answers.CreationDate: 2016-12-17T07:54:57.483Z
Answers.Text: [5] is actually this link: http://yann.lecun.com/exdb/mnist/ ,but you still seem to be right.
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Answers.UserDisplayName: BlindKungFuMaster
Body: <p>I remember reading or hearing a claim that at any point in time since the publication of the MNIST dataset, it has never happened that a method not based on neural networks was the best given the state of science of that point in time.</p>
  
  <p>Is this claim true?</p>
  
Tags: <neural-networks><history>
Title: Neural Networks unmatched on MNIST?
UserDisplayName: liori
ThreadText: # Neural Networks unmatched on MNIST?
  
  (Asked by: username_0 on 2016-12-16)
  
  I remember reading or hearing a claim that at any point in time since the publication of the MNIST dataset, it has never happened that a method not based on neural networks was the best given the state of science of that point in time.
  
  Is this claim true?
  
  **Tags:** neural-networks, history
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2016-12-16. Score: 3 | Accepted answer
  
  To quote the relevant [Wikipedia article](https://en.wikipedia.org/wiki/MNIST_database):
  
  > The original creators of the database keep a list of some of the methods tested on it.[7] In their original paper, they use a support-vector machine to get an error rate of 0.8%
  
  Feel free to look up that original paper, but to me the quote strongly suggests that the first record holder was a support vector machine.
  
  ---
  
  ### Edit
  
  As liori points out the quote is misleading: In the original paper Yann LeCun et al. actually tried a slew of methods and one version of ConvNet scored best (0.7).
  
  But according to the [MNIST-webpage](http://yann.lecun.com/exdb/mnist/), after that initial paper, DeCoste and Scholkopf, MLJ 2002, reached an error of 0.56 with a SVM. So if that webpage is complete, the claim is still false.
  
  > **Comments:**
  > **username_0**: Seems like the paper referenced in [5] (http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf) on Wikipedia states both the V-SVM result of 0.8 and the boosted LeNet-4 result of 0.7, so… not in this case.  
  > **username_1**: [5] is actually this link: http://yann.lecun.com/exdb/mnist/ ,but you still seem to be right.  
  > **username_2**: I don't understand how this answers the original question, which was supposedly asking if any other technique surpassed NNs on the MNIST task, not if before NNs there was a technique that performed better than them, as the OP wrote "**since** the publication of the MNIST dataset, it has never happened".  
  
  ---
  
  