diff --git a/som.cabal b/som.cabal
--- a/som.cabal
+++ b/som.cabal
@@ -1,5 +1,5 @@
 Name:              som
-Version:           7.3.1
+Version:           7.4.0
 Stability:         experimental
 Synopsis:          Self-Organising Maps.
 Description:       A Kohonen Self-organising Map (SOM) maps input patterns 
@@ -33,7 +33,7 @@
 source-repository this
   type:     git
   location: https://github.com/mhwombat/som.git
-  tag:      7.3.1
+  tag:      7.4.0
 
 
 library
diff --git a/src/Data/Datamining/Clustering/SOMInternal.hs b/src/Data/Datamining/Clustering/SOMInternal.hs
--- a/src/Data/Datamining/Clustering/SOMInternal.hs
+++ b/src/Data/Datamining/Clustering/SOMInternal.hs
@@ -159,7 +159,7 @@
 
 -- | Trains the specified node and the neighbourood around it to better
 --   match a target.
---   Most users should use @train@, which automatically determines
+--   Most users should use @'train'@, which automatically determines
 --   the BMU and trains it and its neighbourhood.
 trainNeighbourhood
   :: (Pattern p, G.Grid (gm p), GM.GridMap gm p,
diff --git a/src/Data/Datamining/Clustering/SSOM.hs b/src/Data/Datamining/Clustering/SSOM.hs
--- a/src/Data/Datamining/Clustering/SSOM.hs
+++ b/src/Data/Datamining/Clustering/SSOM.hs
@@ -34,7 +34,7 @@
   (
     -- * Construction
     SSOM(..),
-    Gaussian(..),
+    Exponential(..),
     -- * Deconstruction
     toMap,
     -- * Advanced control
@@ -42,5 +42,5 @@
   ) where
 
 import Data.Datamining.Clustering.SSOMInternal (SSOM(..),
-  Gaussian(..), toMap, trainNode)
+  Exponential(..), toMap, trainNode)
 
diff --git a/src/Data/Datamining/Clustering/SSOMInternal.hs b/src/Data/Datamining/Clustering/SSOMInternal.hs
--- a/src/Data/Datamining/Clustering/SSOMInternal.hs
+++ b/src/Data/Datamining/Clustering/SSOMInternal.hs
@@ -38,7 +38,7 @@
   rate :: f -> LearningRate f -> LearningRate f
 
 -- | A typical learning function for classifiers.
---   @'Gaussian' r0 rf tf@ returns a gaussian function. At time zero,
+--   @'Exponential' r0 rf tf@ returns a gaussian function. At time zero,
 --   the learning rate is @r0@. Over time the learning rate tapers off,
 --   until at time @tf@, the learning rate is @rf@. Normally the
 --   parameters should be chosen such that:
@@ -49,13 +49,13 @@
 --
 --   where << means "is much smaller than" (not the Haskell @<<@
 --   operator!)
-data Gaussian a = Gaussian a a a
+data Exponential a = Exponential a a a
   deriving (Eq, Show, Generic)
 
 instance (Floating a, Fractional a, Num a)
-    => LearningFunction (Gaussian a) where
-  type LearningRate (Gaussian a) = a
-  rate (Gaussian r0 rf tf) t = r0 * ((rf/r0)**(t/tf))
+    => LearningFunction (Exponential a) where
+  type LearningRate (Exponential a) = a
+  rate (Exponential r0 rf tf) t = r0 * ((rf/r0)**(t/tf))
 
 -- | A Simplified Self-Organising Map (SSOM).
 data SSOM f t k p = SSOM
@@ -77,7 +77,7 @@
 toMap = sMap
 
 -- | Trains the specified node to better match a target.
---   Most users should use @train@, which automatically determines
+--   Most users should use @'train'@, which automatically determines
 --   the BMU and trains it.
 trainNode
   :: (Pattern p, LearningFunction f, Metric p ~ LearningRate f,