diff --git a/gklearn/utils/utils.py b/gklearn/utils/utils.py
index fca19dd..5758291 100644
--- a/gklearn/utils/utils.py
+++ b/gklearn/utils/utils.py
@@ -366,19 +366,62 @@ def get_edge_labels(Gn, edge_label):
 def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attrs=None, edge_attrs=None, ds_infos=None, kernel_options={}, **kwargs):
 	if len(kwargs) != 0:
 		kernel_options = kwargs
-	if name == 'Marginalized':
+
+	if name == 'CommonWalk' or name == 'common walk':
+		from gklearn.kernels import CommonWalk
+		graph_kernel = CommonWalk(node_labels=node_labels,
+								 edge_labels=edge_labels,
+								 ds_infos=ds_infos,
+								 **kernel_options)
+
+	elif name == 'Marginalized' or name == 'marginalized':
 		from gklearn.kernels import Marginalized
 		graph_kernel = Marginalized(node_labels=node_labels,
 								 edge_labels=edge_labels,
 								 ds_infos=ds_infos,
 								 **kernel_options)
-	elif name == 'ShortestPath':
+
+	elif name == 'SylvesterEquation' or name == 'sylvester equation':
+		from gklearn.kernels import SylvesterEquation
+		graph_kernel = SylvesterEquation(
+								  ds_infos=ds_infos,
+								  **kernel_options)
+
+	elif name == 'FixedPoint' or name == 'fixed point':
+		from gklearn.kernels import FixedPoint
+		graph_kernel = FixedPoint(node_labels=node_labels,
+								  edge_labels=edge_labels,
+								  node_attrs=node_attrs,
+								  edge_attrs=edge_attrs,
+								  ds_infos=ds_infos,
+								  **kernel_options)
+
+	elif name == 'ConjugateGradient' or name == 'conjugate gradient':
+		from gklearn.kernels import ConjugateGradient
+		graph_kernel = ConjugateGradient(node_labels=node_labels,
+								  edge_labels=edge_labels,
+								  node_attrs=node_attrs,
+								  edge_attrs=edge_attrs,
+								  ds_infos=ds_infos,
+								  **kernel_options)
+
+	elif name == 'SpectralDecomposition' or name == 'spectral decomposition':
+		from gklearn.kernels import SpectralDecomposition
+		graph_kernel = SpectralDecomposition(node_labels=node_labels,
+								  edge_labels=edge_labels,
+								  node_attrs=node_attrs,
+								  edge_attrs=edge_attrs,
+								  ds_infos=ds_infos,
+								  **kernel_options)
+
+	elif name == 'ShortestPath' or name == 'shortest path':
 		from gklearn.kernels import ShortestPath
 		graph_kernel = ShortestPath(node_labels=node_labels,
 								 node_attrs=node_attrs,
 								 ds_infos=ds_infos,
 								 **kernel_options)
-	elif name == 'StructuralSP':
+
+	elif name == 'StructuralSP' or name == 'structural shortest path':
 		from gklearn.kernels import StructuralSP
 		graph_kernel = StructuralSP(node_labels=node_labels,
 								  edge_labels=edge_labels,
@@ -386,25 +429,29 @@ def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attr
 								  edge_attrs=edge_attrs,
 								  ds_infos=ds_infos,
 								  **kernel_options)
-	elif name == 'PathUpToH':
+
+	elif name == 'PathUpToH' or name == 'path up to length h':
 		from gklearn.kernels import PathUpToH
 		graph_kernel = PathUpToH(node_labels=node_labels,
 							  edge_labels=edge_labels,
 							  ds_infos=ds_infos,
 							  **kernel_options)
-	elif name == 'Treelet':
+
+	elif name == 'Treelet' or name == 'treelet':
 		from gklearn.kernels import Treelet
 		graph_kernel = Treelet(node_labels=node_labels,
 							  edge_labels=edge_labels,
 							  ds_infos=ds_infos,
 							  **kernel_options)
-	elif name == 'WLSubtree':
+
+	elif name == 'WLSubtree' or name == 'weisfeiler-lehman subtree':
 		from gklearn.kernels import WLSubtree
 		graph_kernel = WLSubtree(node_labels=node_labels,
 							  edge_labels=edge_labels,
 							  ds_infos=ds_infos,
 							  **kernel_options)
-	elif name == 'WeisfeilerLehman':
+
+	elif name == 'WeisfeilerLehman' or name == 'weisfeiler-lehman':
 		from gklearn.kernels import WeisfeilerLehman
 		graph_kernel = WeisfeilerLehman(node_labels=node_labels,
 							  edge_labels=edge_labels,
@@ -541,10 +588,18 @@ def get_mlti_dim_edge_attrs(G, attr_names):
 
 def normalize_gram_matrix(gram_matrix):
 	diag = gram_matrix.diagonal().copy()
+	old_settings = np.seterr(invalid='raise') # Catch FloatingPointError: invalid value encountered in sqrt.
 	for i in range(len(gram_matrix)):
 		for j in range(i, len(gram_matrix)):
-			gram_matrix[i][j] /= np.sqrt(diag[i] * diag[j])
-			gram_matrix[j][i] = gram_matrix[i][j]
+			try:
+				gram_matrix[i][j] /= np.sqrt(diag[i] * diag[j])
+			except:
+# 				rollback()
+				np.seterr(**old_settings)
+				raise
+			else:
+				gram_matrix[j][i] = gram_matrix[i][j]
+	np.seterr(**old_settings)
 	return gram_matrix