SVM Weighting

lexusboylexusboy Member Posts: 22 Maven

I would be grateful to anyone who could explain me the working of SVM Weighting, I have used RapidMiner in extensively in my thesis, and I have to explain these operators in my document. I read the description of SVM Weighting in the RapidMIner manual but I could not understand it well.




  • BenBen Member Posts: 13 Contributor II
    a linear SVM (support vector machine), in very short, builds a linear model R^d -> {+1, -1} given by the equation
    Y = sign(w*X+b)
    Y = sign( sumdi=1(wi*Xi) +b).
    The absolute value of the ith entrance in the weight-vector w resembles the influence of the ith feature, given the all features are normalized.
    The SVMWeighting operator now calculates such a linear model and returns the absolutes values of the weight vector.

    You should note, that two features which are highly correlated share the importance score, giving them a lower score even if they are "important" for the class variable. For this case, you should better try Recursive Feature Elimination (RFE)

    For a better description of SVM see
    and for Recursive Feature Elimination using the weight vector of an SVM see
    author = "Isabelle Guyon and Jason Weston and Stephen Barnhill and Vladimir Vapnik",
    title = "Gene Selection for Cancer Classification using Support Vector Machines",
    journal = "Machine Learning",
    volume = "46",
    number = "1-3",
    publisher = "Kluwer Academic Publishers, Boston",
    pages = "389--422",
    year = "2002",

  • B_MinerB_Miner Member Posts: 72 Maven

    A very readable book on SVM is by Dr. Lutz Hamel, published by Wiley.

  • lexusboylexusboy Member Posts: 22 Maven
    Thank you Ben for your explanation & B_Miner for your suggestion, I will look into that book
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