Multi-Class Labeling using the Bayes Point Machine RRS feed

  • Question

  • Hi, I am having trouble adapting the Bayes Point Machine provided in the examples to work with my data set. I am trying to classify the first two points (a data pair) into one of 4 possible classes (0 to 3). Below is a sample of the data (it is just a small sample, have over 10,000 points in total).


    Could someone please advise me as to how the Main for the example should look like to classify the data? I have about 6 different versions at the moment and none seem to work...


    Thursday, February 7, 2013 2:29 AM


All replies

  • Please see http://social.microsoft.com/Forums/en-US/infer.net/thread/4ceaf7ef-1110-43b8-8037-48ca8ceddf01. Let me know if you still have questions. By the way, remember to add a constant bias input to your feature vector.


    Thursday, February 7, 2013 3:22 PM
  • Thank you very much John! Just to double check - does the constant bias have any impact on the inference based on its value? By this I mean would the result be different if the bias constant is 0 or 1?
    Thursday, February 7, 2013 5:08 PM
  • The bias constant has to be non-zero. 1 is a good value for it.

    Your other features should be standardised - i.e. each feature scaled and offset to be roughly between 0 and 1 or -1 and 1. If not you will need to think much more carefully about priors.

    Thursday, February 7, 2013 5:44 PM
  • Would this approach have to be used for the Sparse BPM as well? The reason I ask is because I have written a class with a SparseBPM and a standard BPM, I supplied both with the same data, and ensured (as far as I can see) that the settings are the same on both (eg: inference algorithm, noise, etc...), and somehow I get two different results. The standard BPM is doing fine, however the SparseBPM is leaning significantly towards one class. Is there an inherent difference between the two BPMs?

    *The data supplied is not sparse to ensure that the standard BPM performs correctly.
    Sunday, April 7, 2013 11:32 AM