diff --git a/som.cabal b/som.cabal
--- a/som.cabal
+++ b/som.cabal
@@ -1,5 +1,5 @@
 name:           som
-version:        4.2
+version:        5.0
 synopsis:       Self-Organising Maps
 description:    A Kohonen Self-organising Map (SOM) maps input patterns 
                 onto a regular grid (usually two-dimensional) where each
diff --git a/src/Data/Datamining/Clustering/Classifier.hs b/src/Data/Datamining/Clustering/Classifier.hs
--- a/src/Data/Datamining/Clustering/Classifier.hs
+++ b/src/Data/Datamining/Clustering/Classifier.hs
@@ -34,8 +34,8 @@
   models ∷ c k p → [p]
 
   -- | @'differences' c target@ returns the indices of all nodes in 
-  --   @c@, paired with the difference between @target@ and the node's 
-  --   model.
+  --   @c@, paired with the difference between @target@ and the 
+  --   node's model.
   differences ∷ (Pattern p, v ~ Metric p) ⇒ c k p → p → [(k, v)]
 
   -- | @classify c target@ returns the index of the node in @c@ 
@@ -59,7 +59,8 @@
   --   index of the node in @c@ whose model best matches the input
   --   @target@, and a modified copy of the classifier @c@ that has
   --   partially learned the @target@. Invoking @classifyAndTrain c p@
-  --   may be faster than invoking @(p `classify` c, train c p)@, but they
+  --   may be faster than invoking @(p `classify` c, train c p)@, but 
+  --   they
   --   should give identical results.
   classifyAndTrain 
     ∷ (Ord v, v ~ Metric p) ⇒ 
diff --git a/src/Data/Datamining/Clustering/SOM.hs b/src/Data/Datamining/Clustering/SOM.hs
--- a/src/Data/Datamining/Clustering/SOM.hs
+++ b/src/Data/Datamining/Clustering/SOM.hs
@@ -7,17 +7,29 @@
 -- Stability   :  experimental
 -- Portability :  portable
 --
--- A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto a 
--- regular grid (usually two-dimensional) where each node in the grid is
--- a model of the input data, and does so using a method which ensures 
--- that any topological relationships within the input data are also 
--- represented in the grid. This implementation supports the use of 
--- non-numeric patterns.
+-- A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto
+-- a regular grid (usually two-dimensional) where each node in the grid
+-- is a model of the input data, and does so using a method which
+-- ensures that any topological relationships within the input data are
+-- also represented in the grid. This implementation supports the use
+-- of non-numeric patterns.
 --
 -- In layman's terms, a SOM can be useful when you you want to discover
 -- the underlying structure of some data. A tutorial is available at
 -- <https://github.com/mhwombat/som/wiki>.
 --
+-- NOTES: 
+--
+-- * Version 5.0 fixed a bug in the @`decayingGaussian`@ function. If
+--   you use @`defaultSOM`@ (which uses this function), your SOM
+--   should now learn more quickly.
+--
+-- * The @gaussian@ function has been removed because it is not as
+--   useful for SOMs as I originally thought. It was originally designed
+--   to be used as a factor in a learning function. However, in most
+--   cases the user will want to introduce a time decay into the
+--   exponent, rather than simply multiply by a factor.
+--
 -- References:
 --
 -- * Kohonen, T. (1982). Self-organized formation of topologically 
@@ -32,7 +44,6 @@
     SOM,
     defaultSOM,
     customSOM,
-    gaussian,
     decayingGaussian,
     -- * Deconstruction
     toGridMap,
@@ -42,6 +53,6 @@
   ) where
 
 import Data.Datamining.Clustering.SOMInternal (SOM, defaultSOM,
-  customSOM, gaussian, decayingGaussian, toGridMap, trainNeighbourhood,
+  customSOM, decayingGaussian, toGridMap, trainNeighbourhood,
   incrementCounter)
 
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
@@ -20,7 +20,6 @@
     SOM(..),
     defaultSOM,
     customSOM,
-    gaussian,
     decayingGaussian,
     -- * Deconstruction
     toGridMap,
@@ -149,7 +148,7 @@
 --   at "time" zero. The BMU is the model which best matches the
 --   current target pattern.
 --
---   [@w@] The width of the bell curve at "time" zero.
+--   [@w@] The width of the bell curve at time zero.
 --
 --   [@t@] Controls how rapidly the learning rate decays. After this
 --   time, any learning done by the classifier will be negligible.
@@ -190,19 +189,6 @@
         sCounter=0
       }
 
--- | Calculates @r/e/^(-d^2/2w^2)@.
---   This form of the Gaussian function is useful as a learning rate
---   function. In @'gaussian' r w d@, @r@ specifies the highest learning
---   rate, which will be applied to the SOM node that best matches the
---   input pattern. The learning rate applied to other nodes will be
---   applied based on their distance @d@ from the best matching node.
---   The value @w@ controls the \'width\' of the Gaussian. Higher values
---   of @w@ cause the learning rate to fall off more slowly with
---   distance @d@.
-gaussian ∷ Floating a ⇒ a → a → Int → a
-gaussian r w d = r * exp (-d'*d'/(2*w*w))
-  where d' = fromIntegral d
-
 -- | Configures a typical learning function for classifiers.
 --   @'decayingGaussian' r w0 tMax@ returns a bell curve-shaped
 --   function. At time zero, the maximum learning rate (applied to the
@@ -211,6 +197,8 @@
 --   @tMax@, the learning rate is negligible.
 decayingGaussian
   ∷ Floating a ⇒ a → a → Int → (Int → Int → a)
-decayingGaussian r w0 tMax =
-  \t d → let t' = fromIntegral t in gaussian r w0 d * exp (-t'/tMax')
-  where tMax' = fromIntegral tMax
+decayingGaussian r w0 tMax t d = r * s * exp (-(d'*d')/(2*w0*w0*s*s))
+    where s = exp (-t'/tMax')
+          t' = fromIntegral t
+          tMax' = fromIntegral tMax
+          d' = fromIntegral d