14th International Congress of Phonetic Sciences (ICPhS-14)
San Francisco, CA, USA
A number of error measures will be discussed that are
used for training and testing classification algorithms
designed to simulate human classification behaviour.
These are (1) the error criterion based on multinomial
decision strategy (average log likelihood ratio), (2) the
mean squared error (MSE) based on the L2 (Euclidean
metric), (3) an error criterion based on similarity, (4) a
novel one, the average log likelihood ratio. We will not
focus on particular minimalization methods that are
inherent to specific numerical minimization schemes,
such as the back propagation method, stochastic annealing,
etc., but rather on the conceptual differences
of the error measures mentioned.
The classifiers are implemented by means of a feedforward network. The training will be considered to be supervised in all cases.
Bibliographic reference. Bosch, Louis ten / Smits, Roel (1999): "On error criteria in human classification modeling", In ICPhS-14, 1949-1952.