Answers.Body: <p>It's possible that in some applications one can train each Adaline unit individually which is a key sparse connection feature of Madaline networks, each Adaline unit specializes in learning a linear decision boundary for its specific subset of all the input data. By training each Adaline unit independently, the network can effectively handle complex classification problems where different features are important for different subsets of all the inputs.</p>
  <p>For instance, for a binary classification problem where you want to determine whether an email is spam or not based on two features (the number of spam words and the number of non-spam words), a Madaline network can be employed consisting of two Adaline units in the hidden layer, each responsible for training one feature. Finally after individual training they can be combined to form a simple decision rule: if the output of the first Adaline unit is positive, classify the email as spam, else if the output of the second Adaline unit is positive, classify the email as non-spam.</p>
  
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Answers.CreationDate: 2023-11-06T22:25:57.463Z
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Body: <p>I've heard that Madaline networks generally have a single hidden layer, which have multiple Adaline units, where each Adaline receives different subsets of the input data. And the output of the network is determined using some decision rule to combine the outputs of the Adaline units, producing the classification result. So,each Adaline is independently trained.</p>
  <p><strong>That is correct?
  Can I train a Madaline network, just training each neuron individually, and then combining the output of all of them to be the classification result?</strong></p>
  
Tags: <neural-networks>
Title: Can I train a Madaline network, just training each neuron individually, and then combining the output of all of them to be the classification result?
UserDisplayName: will The J
ThreadText: # Can I train a Madaline network, just training each neuron individually, and then combining the output of all of them to be the classification result?
  
  (Asked by: username_0 on 2023-11-05)
  
  I've heard that Madaline networks generally have a single hidden layer, which have multiple Adaline units, where each Adaline receives different subsets of the input data. And the output of the network is determined using some decision rule to combine the outputs of the Adaline units, producing the classification result. So,each Adaline is independently trained.
  
  **That is correct?
  Can I train a Madaline network, just training each neuron individually, and then combining the output of all of them to be the classification result?**
  
  **Tags:** neural-networks
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-11-06. Score: 1
  
  It's possible that in some applications one can train each Adaline unit individually which is a key sparse connection feature of Madaline networks, each Adaline unit specializes in learning a linear decision boundary for its specific subset of all the input data. By training each Adaline unit independently, the network can effectively handle complex classification problems where different features are important for different subsets of all the inputs.
  
  For instance, for a binary classification problem where you want to determine whether an email is spam or not based on two features (the number of spam words and the number of non-spam words), a Madaline network can be employed consisting of two Adaline units in the hidden layer, each responsible for training one feature. Finally after individual training they can be combined to form a simple decision rule: if the output of the first Adaline unit is positive, classify the email as spam, else if the output of the second Adaline unit is positive, classify the email as non-spam.
  
  
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