A Single-Pass Classifier for Categorical Data
  This paper describes a new method for classifying a dataset that partitions
elements into their categories. It has relations with neural networks but a
slightly different structure, requiring only a single pass through the
classifier to generate the weight sets. A grid-like structure is required as
part of a novel idea of converting a 1-D row of real values into a 2-D
structure of value bands. Each cell in any band then stores a distinct set of
weights, to represent its own importance and its relation to each output
category. During classification, all of the output weight lists can be
retrieved and summed to produce a probability for what the correct output
category is. The bands possibly work like hidden layers of neurons, but they
are variable specific, making the process orthogonal. The construction process
can be a single update process without iterations, making it potentially much
faster. It can also be compared with k-NN and may be practical for partial or
competitive updating.
