diff --git a/docs/source/library.rst b/docs/source/library.rst
index 4e8331e..0472f05 100644
--- a/docs/source/library.rst
+++ b/docs/source/library.rst
@@ -74,7 +74,7 @@ For a test sample they return a distribution instead of a class assignment.
 The following two algorihms were presented by :cite:t:`seo2003` . 
 Every prototypes is a center of a gaussian distribution of its class, generating a mixture model.
 
-.. autoclass:: prototorch.models.probabilistic.LikelihoodRatioLVQ
+.. autoclass:: prototorch.models.probabilistic.SLVQ
    :members:
 
 .. autoclass:: prototorch.models.probabilistic.RSLVQ
diff --git a/prototorch/models/__init__.py b/prototorch/models/__init__.py
index 48c4835..73ac1ee 100644
--- a/prototorch/models/__init__.py
+++ b/prototorch/models/__init__.py
@@ -19,7 +19,7 @@ from .glvq import (
 )
 from .knn import KNN
 from .lvq import LVQ1, LVQ21, MedianLVQ
-from .probabilistic import CELVQ, RSLVQ, LikelihoodRatioLVQ
+from .probabilistic import CELVQ, RSLVQ, SLVQ
 from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
 from .vis import *
 
diff --git a/prototorch/models/probabilistic.py b/prototorch/models/probabilistic.py
index 4f6737f..475fdc0 100644
--- a/prototorch/models/probabilistic.py
+++ b/prototorch/models/probabilistic.py
@@ -61,8 +61,8 @@ class ProbabilisticLVQ(GLVQ):
         return loss
 
 
-class LikelihoodRatioLVQ(ProbabilisticLVQ):
-    """Learning Vector Quantization based on Likelihood Ratios."""
+class SLVQ(ProbabilisticLVQ):
+    """Soft Learning Vector Quantization."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss = LossLayer(nllr_loss)