Description: PERCEPTRON Perceptron algorithm to train binary linear classifier.
Synopsis:
model = perceptron(data)
model = perceptron(data,options)
model = perceptron(data,options,init_model)
Description:
model = perceptron(data) uses the Perceptron learning rule
to find separating hyperplane from given binary labeled data.
model = perceptron(data,options) specifies stopping condition of
the algorithm in structure options:
.tmax [1x1]... maximal number of iterations.
If tmax==-1 then it only returns index (model.last_update)
of data vector which should be used by the algorithm for updating
the linear rule in the next iteration.
model = perceptron(data,options,init_model) specifies initial model
which must contain:
.W [dim x 1] ... normal vector.
.b [1x1] ... bias of hyperplane.
.t [1x1] (optional) ... iteration number.
Input:
data [struct] Labeled (binary) training data.
.X [dim x num
To Search:
File list (Check if you may need any files):
perceptron.m
Download