Issue 1:
as reported here:

http://sourceforge.net/mailarchive/forum.php?thread_name=20110610134529.GA6992%40phare.normalesup.org&forum_name=scikit-learn-general

Issue 2:
I am finding test_svm.py:test_svc_clone_with_callable_kernel to be flaky. I think there is maybe some randomness sneaking in. I got this on two machines. I am getting the errors in Python3 though, so I wanted to double check whether this is possibly a python3 bug before digging in too heavily (or if this is a known flaky test)

These two runs are right after eachother without any code changes.

(scikit-learn) justinvf@alf:~/github/justinvf$ nosetests --exe scikit-learn/sklearn/svm/tests/test_svm.py -s
I: Seeding RNGs with 1127491369
........................[LibLinear]
Cloned dual coef: [[ 0.25 -0.25]]
builint dual coef: [[ 0.25 -0.25]]

Cloned intercept: [-0.]
builint intercept: [-0.]

cloned predicted proba: [[ 0.83311448 0.16688552]
[ 0.73627062 0.26372938]
[ 0.83311448 0.16688552]
[ 0.21446606 0.78553394]
[ 0.13232847 0.86767153]
[ 0.13232847 0.86767153]]
builtin predicted proba: [[ 0.82938691 0.17061309]
[ 0.74157903 0.25842097]
[ 0.82938691 0.17061309]
[ 0.25841623 0.74158377]
[ 0.17053327 0.82946673]
[ 0.17053327 0.82946673]]
F..
======================================================================
FAIL: sklearn.svm.tests.test_svm.test_svc_clone_with_callable_kernel
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/justinvf/virtualenvs/scikit-learn/lib/python3.3/site-packages/nose/case.py", line 198, in runTest
self.test(*self.arg)
File "/home/justinvf/github/justinvf/scikit-learn/sklearn/svm/tests/test_svm.py", line 638, in test_svc_clone_with_callable_kernel
svm_builtin.predict_proba(X))
File "/home/justinvf/virtualenvs/scikit-learn/lib/python3.3/site-packages/numpy/testing/utils.py", line 812, in assert_array_almost_equal
header=('Arrays are not almost equal to %d decimals' % decimal))
File "/home/justinvf/virtualenvs/scikit-learn/lib/python3.3/site-packages/numpy/testing/utils.py", line 645, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 6 decimals

(mismatch 100.0%)
x: array([[ 0.83311448, 0.16688552],
[ 0.73627062, 0.26372938],
[ 0.83311448, 0.16688552],...
y: array([[ 0.82938691, 0.17061309],
[ 0.74157903, 0.25842097],
[ 0.82938691, 0.17061309],...

----------------------------------------------------------------------
Ran 27 tests in 0.193s

FAILED (failures=1)
(scikit-learn) justinvf@alf:~/github/justinvf$ nosetests --exe scikit-learn/sklearn/svm/tests/test_svm.py -s
I: Seeding RNGs with 245319400
........................[LibLinear]
Cloned dual coef: [[ 0.25 -0.25]]
builtin dual coef: [[ 0.25 -0.25]]

Cloned intercept: [-0.]
builtin intercept: [-0.]

cloned predicted proba: [[ 0.83518712 0.16481288]
[ 0.74780932 0.25219068]
[ 0.83518712 0.16481288]
[ 0.25796172 0.74203828]
[ 0.1689573 0.8310427 ]
[ 0.1689573 0.8310427 ]]
builtin predicted proba: [[ 0.83518712 0.16481288]
[ 0.74780932 0.25219068]
[ 0.83518712 0.16481288]
[ 0.25796172 0.74203828]
[ 0.1689573 0.8310427 ]
[ 0.1689573 0.8310427 ]]
...
----------------------------------------------------------------------
Ran 27 tests in 0.191s

OK

Here is the code (added the print statements that you see above):

def test_svc_clone_with_callable_kernel():
# create SVM with callable linear kernel, check that results are the same
# as with built-in linear kernel
svm_callable = svm.SVC(kernel=lambda x, y: np.dot(x, y.T),
probability=True)
# clone for checking clonability with lambda functions..
svm_cloned = base.clone(svm_callable)
svm_cloned.fit(X, Y)

svm_builtin = svm.SVC(kernel='linear', probability=True)
svm_builtin.fit(X, Y)

# import pdb; pdb.set_trace()
print('\nCloned dual coef: ', svm_cloned.dual_coef_,
'\nbuiltin dual coef: ', svm_builtin.dual_coef_)

assert_array_almost_equal(svm_cloned.dual_coef_,
svm_builtin.dual_coef_)

print('\nCloned intercept: ', svm_cloned.intercept_,
'\nbuiltin intercept: ', svm_builtin.intercept_)

assert_array_almost_equal(svm_cloned.intercept_,
svm_builtin.intercept_)
assert_array_equal(svm_cloned.predict(X), svm_builtin.predict(X))

print('\ncloned predicted proba: ', svm_cloned.predict_proba(X),
'\nbuiltin predicted proba: ', svm_builtin.predict_proba(X))

assert_array_almost_equal(svm_cloned.predict_proba(X),
svm_builtin.predict_proba(X))
assert_array_almost_equal(svm_cloned.decision_function(X),
svm_builtin.decision_function(X))

This works maybe 10% of the time.