diff --git a/GPy/models/fitc_classification.py b/GPy/models/fitc_classification.py
index f4cf4e8d..ee92a1b4 100644
--- a/GPy/models/fitc_classification.py
+++ b/GPy/models/fitc_classification.py
@@ -31,8 +31,8 @@ class FITCClassification(FITC):
             kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
 
         if likelihood is None:
-            distribution = likelihoods.likelihood_functions.Binomial()
-            likelihood = likelihoods.EP(Y, distribution)
+            noise_model = likelihoods.binomial()
+            likelihood = likelihoods.EP(Y, noise_model)
         elif Y is not None:
             if not all(Y.flatten() == likelihood.data.flatten()):
                 raise Warning, 'likelihood.data and Y are different.'
diff --git a/GPy/models/sparse_gp_classification.py b/GPy/models/sparse_gp_classification.py
index 9228fb89..4e4e4316 100644
--- a/GPy/models/sparse_gp_classification.py
+++ b/GPy/models/sparse_gp_classification.py
@@ -28,11 +28,11 @@ class SparseGPClassification(SparseGP):
 
     def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+            kernel = kern.rbf(X.shape[1])# + kern.white(X.shape[1],1e-3)
 
         if likelihood is None:
-            distribution = likelihoods.likelihood_functions.Binomial()
-            likelihood = likelihoods.EP(Y, distribution)
+            noise_model = likelihoods.binomial()
+            likelihood = likelihoods.EP(Y, noise_model)
         elif Y is not None:
             if not all(Y.flatten() == likelihood.data.flatten()):
                 raise Warning, 'likelihood.data and Y are different.'