Answers.Body: <p>i asked,i answered:</p>
  <p><a href="https://i.sstatic.net/tZwO9.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/tZwO9.jpg" alt="Teuvo KOHONEN, &quot;The Self-Organizing Map&quot;, Proceedings of The IEEE, Vol. 78, No. 9 September 1990" /></a></p>
  <p>Teuvo KOHONEN, &quot;The Self-Organizing Map&quot;, Proceedings of The IEEE, Vol. 78, No. 9 September 1990 page.1471</p>
  
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Answers.Text: Can you please at least copy and paste the actual text that you're showing us rather than pasting a screenshot? In addition to that, you should also summarize the main points of the quote that answer your question.
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Body: <p>I'm developing a neural network software using several NN architectures including LVQ family. I met a parameter that is used in the 3rd condition of LVQ2 and later versions. It's named <span class="math-container">$w$</span> and is used to calculate whether the input vector falls in the <span class="math-container">$w$</span>-length window gap or not. For example, for LVQ 2.1 it's used to calculate the 3rd condition as follows:</p>
  <p><span class="math-container">\begin{equation}
  \min(d_{i}/d_{j},d_{j}/d_{i})&gt;\frac{1-w}{1+w},0&lt;w&lt;1.
  \end{equation}</span></p>
  <p>where</p>
  <ul>
  <li><span class="math-container">$d_{i}$</span> is the minimum Euclidean distance from input vector (1st winner neuron's distance)</li>
  <li><span class="math-container">$d_{j}$</span> is the 2nd minimum Euclidean distance from input vector (2nd winner neuron's distance)</li>
  </ul>
  <p>This parameter <span class="math-container">$w$</span> is a user-determined one like learning rate or momentum, etc. I give the value of <span class="math-container">$w$</span> to training algorithm manually but this is a trial and error style. During my research, I found an information about this parameter may be calculated via training samples count.</p>
  <p>Are there any information about how to calculate this parameter by training samples count or something else like represented training samples count to the network?</p>
  
Tags: <neural-networks><machine-learning><deep-learning><supervised-learning>
Title: How can I calculate the parameter $w$ in the third condition of LVQ 2.1 algorithm?
UserDisplayName: uygur
ThreadText: # How can I calculate the parameter $w$ in the third condition of LVQ 2.1 algorithm?
  
  (Asked by: username_0 on 2022-02-10)
  
  I'm developing a neural network software using several NN architectures including LVQ family. I met a parameter that is used in the 3rd condition of LVQ2 and later versions. It's named $w$ and is used to calculate whether the input vector falls in the $w$-length window gap or not. For example, for LVQ 2.1 it's used to calculate the 3rd condition as follows:
  
  \begin{equation}
  \min(d\_{i}/d\_{j},d\_{j}/d\_{i})>\frac{1-w}{1+w},0
  
  where
  
  * $d\_{i}$ is the minimum Euclidean distance from input vector (1st winner neuron's distance)
  * $d\_{j}$ is the 2nd minimum Euclidean distance from input vector (2nd winner neuron's distance)
  
  This parameter $w$ is a user-determined one like learning rate or momentum, etc. I give the value of $w$ to training algorithm manually but this is a trial and error style. During my research, I found an information about this parameter may be calculated via training samples count.
  
  Are there any information about how to calculate this parameter by training samples count or something else like represented training samples count to the network?
  
  **Tags:** neural-networks, machine-learning, deep-learning, supervised-learning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-04-13. Score: -1
  
  i asked,i answered:
  
  [![Teuvo KOHONEN, ](https://i.sstatic.net/tZwO9.jpg)](https://i.sstatic.net/tZwO9.jpg)
  
  Teuvo KOHONEN, "The Self-Organizing Map", Proceedings of The IEEE, Vol. 78, No. 9 September 1990 page.1471
  
  > **Comments:**
  > **username_1**: Can you please at least copy and paste the actual text that you're showing us rather than pasting a screenshot? In addition to that, you should also summarize the main points of the quote that answer your question.  
  
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