Tabata is a concept learning algorithm. Given a learning set of
instances of k disjoint classes, Tabata outputs a concept description
for each class. Each description is made of a variable number of
conjunctive terms. Each term is expressed as a list of atoms. Instances
also are represented as lists of atoms negative litterals have
explicitly defined as atoms).
A decision procedure is provided in order to classify test instances.
Tabata is fully described in the ECAI98 paper
.You can download here
the sources (in C) and binaries (running Solaris on Sun workstations)
of tabata together with some examples as a tar
file . May be you would like
to have a look to the ReadMe
file and to its companion file CodeMe concerning
the atomic representation.