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Train a single neuron perceptron to classify the Iris dataset provided with
this homework. The dataset consists of a l 50x3 matrix. Columns 1 and 2 of
the data represent the two-dimensional input features, and column 3 contains
the class labels. Each of the data samples belongs to one of two varieties of
the Iris plant.
a. Is this dataset linearly separable? Show your result graphically.
b. Implement this network in MATLAB without using the neural
network toolbox. Separate the da ta into two sets, and use one set for
training the network and the other for testing the trained netw ork. You
can use a 70:30 split where 70% of the data is used for training and
Train a single neuron perceptron to classify the Iris dataset provided with
this homework. The dataset consists of a l 50x3 matrix. Columns 1 and 2 of
the data represent the two-dimensional input features, and column 3 contains
the class labels. Each of the data samples belongs to one of two varieties of
the Iris plant.
a.
Is this dataset linearly separable? Show your result graphically.
b. Implement this network in MATLAB without using the neural
network toolbox. Separate the da ta into two sets, and use one set for
training the network and the other for testing the trained network. You
can use a 70:30 split where 70% of the data is used for training and
30% for testing the network.
c. Plot the mean squared error curve also called the learning curve.
d. Compute the percentage of misclassified testing samples.
e.
Plot the
2-dimensional
error surface
for this
problem
by
varying
each
of the
weights between [-100
,
100].
f
.
Study
the
impact
that
varying
the
initial
weight
vector
has
on
the
learning
curve
and
the
number
of
iterations
it
takes
the
algorithm
to
converge
.
Explain
your
observations
with
respect
to
the
error
surface
you plotted for part
d.
% for testing the network.
c. Plot the mean squared error curve also called the learning curve.
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d. Compute the percentage of misclassified testing samples
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