RMKL (version 1.0)

SEMKL.classification: Simple and Efficient MKL

Description

This function conducts Simple and Efficnent MKL for precomputed gramm matrices

Usage

SEMKL.classification(k, outcome, penalty, tol = 1e-04,
  max.iters = 1000)

Arguments

k

list of Gramm matrices

outcome

vector of binary outcome -1 and 1

penalty

penalty of the smoothness of the resulting desicion rules

tol

change between to iterations is smaller than this, algorithms is considered to have converged

max.iters

maximum number of allowed iteratons

Value

gamma weight vector for the importnace of each kernel

alpha coeffiencents of the dual of MKL

time total amount of time to train model

iters Numvber of iterations to reach convergence criteria

gamma_all Kernel weights for each interation of SEMKL

Examples

# NOT RUN {
data(benchmark.data)
example.data=benchmark.data[[1]]
#Load data
training.samples=sample(1:dim(example.data)[1],floor(0.7*dim(example.data)[1]),replace=FALSE)
# Split samples into training and test sets 
C=1
kernels=c('radial','polynomial')
degree=c(0,2)
scale=c(0,2)
sigma=c(2,0)
K=kernels.gen(example.data[,1:2], training.samples, kernels, degree, scale, sigma)
K.train=K$K.train
SEMKL.classification(K.train,example.data[training.samples,3], C)
# }