title: How to approach Machine Learning problems with dynamically sized input collection?
body: <p>I'm approaching a problem trying to classify a data sample as good or bad quality with machine learning.</p>
  
  <p>The data sample is stored in a relational database. A sample contains the attributes id, name, number of up-votes (for good/bad quality indication), number of comments, etc. Also there is a table that has items with foreign keys pointing to a data sample id. The items contain a weight and a name. All items together pointing to a data sample characterizes the data sample, which typically could help classify the data sample. The problem is, that the number of items pointing to one foreign key is different for different samples.</p>
  
  <p>I want to feed the Machine Learning input, of e.g. a neural network, with the items that point to a specific data sample. The problem is that I don't know the number of items, so I don't know how many input nodes I want. </p>
  
  <p>Q1) Is it possible to use neural networks when the input dimension is dynamic? If so, how?</p>
  
  <p>Q2) Are there any best practices for feeding a network with a list of tuples, when the length of the list is unknown?</p>
  
  <p>Q3) Are there any best practices for applying machine learning to relational databases?</p>