These are the most frequently used words in this book.
algorithms
analysis
approach
asymptotic
bayes
between
boundary
case
classes
classification
classifier
complexity
conditional
cost
covariance
curve
data
decision
density
depends
design
df
different
dimensionality
discriminant
distance
distribution
edc
empirical
equation
error
estimate
example
expected
features
figure
first
fisher
function
gaussian
generalisation
hidden
however
increase
iterations
large
layer
learning
let
linear
matrix
mean
method
minimum
mlp
model
multivariate
need
network
neural
noise
number
obtain
obtained
optimal
order
output
parameters
pattern
perceptron
performance
prior
probability
problem
random
results
rule
sample
section
see
selection
set
size
slp
small
space
standard
statistical
term
therefore
thus
training
two
use
used
values
variables
variance
vectors
weights