Based on Weston (2000) Feature Selection for SVMs.
Creates matrix X
and vector Y
with six dimensions out of 202 relevant and
equal probability of y
= 1 or -1.
With a prob of 0.7 we draw xi = y * norm(i, 1)
for i
= 1, 2, 3 and
xi
= norm(0, 1)
for i
= 4, 5, 6.
Otherwise: xi = norm(0, 1)
for i
= 1, 2, 3 and xi = y * norm(i - 3, 1)
for i
= 4, 5, 6.
All other features are noise.
create.linear.toy.data(n)
list(X = [Matrix], Y = [vector], orig.features = logical)
[integer(1)]
number of samples to draw.
Other Artificial Datasets:
clonetask()
,
create.hypersphere.data()
,
create.linear.data()
,
create.regr.task()
,
task.add.permuted.cols()
,
task.add.random.cols()