Description Usage Arguments Details Value Functions Author(s) Examples
Linearly Programmed L1loss Linear Support Vector Machine with L1 regularization
1 2 3 4 5 6 7 8 9 
x 
a numeric data matrix to predict 
y 
a response factor for each row of x. It must be a 2 levels factor for svmLP, or a >=2 levels factor for svmMulticlassLP 
LAMBDA 
control the regularization strength in the optimization process. This is the value used as coefficient of the regularization term. 
loss.weights 
numeric vector of loss weights to incure for each instance of x. Vector length should match length(y), but values are cycled if not of identical size. 
object 
an object of class svmLP or svmMLP 
... 
unused, present to satisfy the generic predict() prototype 
svmLP solves a linear program implementing a linear SVM with L1 regularization and L1 loss. It solves: min_w LAMBDA*w + sum_i(e_i); s.t. y_i * <w.x_i> >= 1e_i; e_i >= 0 where w is the L1norm of w
svmMulticlassLP solves a linear program implementing multiclassSVM with L1 regularization and L1 loss. It solves: min_w LAMBDA*w + sum_i(e_i); s.t. <w.x_i>  <w.x_j> >= 1e_i; e_i >= 0 where w is the L1norm of w
the optimized weights matrix, with class svmLP
predict() return predictions for row of x, with an attribute "decision.value"
predict() return predictions for row of x, with an attribute "decision.value"
svmLP
: linear programm solving binarySVM with L1regularization and L1norm
svmMulticlassLP
: linear programm solving multiclassSVM with L1regularization and L1norm
Julien Prados
1 2 3 4 5 6 7  x < cbind(100,data.matrix(iris[1:4]))
y < iris$Species
w < svmMulticlassLP(x,y)
table(predict(w,x),y)
w < svmLP(x,y=="setosa")
table(predict(w,x),y)

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