Last data update: 2014.03.03
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R: SILF Loss
SILF Loss
Description
Minimizes soft insensitive loss function (SILF) for support vector regression.
Usage
iSolver(z, a, extra)
Arguments
z |
Vector containing observed response
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a |
Matrix with active constraints
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extra |
List with element y containing the observed response vector, weights
with optional observation weights, beta between 0 and 1, and eps > 0
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Details
This function is called internally in activeSet by setting mySolver = iSolver .
Value
x |
Vector containing the fitted values
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lbd |
Vector with Lagrange multipliers
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f |
Value of the target function
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gx |
Gradient at point x
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References
Efron, B. (1991). Regression percentiles using asymmetric squared error loss. Statistica Sinica, 1, 93-125.
See Also
activeSet
Examples
##Fitting isotone regression using active set
set.seed(12345)
y <- rnorm(9) ##response values
w <- rep(1,9) ##unit weights
eps <- 2
beta <- 0.4
btota <- cbind(1:8, 2:9) ##Matrix defining isotonicity (total order)
fit.silf <- activeSet(btota, iSolver, weights = w, y = y, beta = beta, eps = eps)
Results
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