<s>
Generalized	O
regression	O
neural	B-Architecture
network	I-Architecture
(	O
GRNN	O
)	O
is	O
a	O
variation	O
to	O
radial	B-Algorithm
basis	I-Algorithm
neural	B-Architecture
networks	I-Architecture
.	O
</s>
<s>
GRNN	O
can	O
be	O
used	O
for	O
regression	O
,	O
prediction	O
,	O
and	O
classification	B-General_Concept
.	O
</s>
<s>
GRNN	O
represents	O
an	O
improved	O
technique	O
in	O
the	O
neural	B-Architecture
networks	I-Architecture
based	O
on	O
the	O
nonparametric	O
regression	O
.	O
</s>
<s>
The	O
idea	O
is	O
that	O
every	O
training	O
sample	O
will	O
represent	O
a	O
mean	O
to	O
a	O
radial	B-Algorithm
basis	I-Algorithm
neuron	O
.	O
</s>
<s>
is	O
the	O
Radial	B-Algorithm
basis	I-Algorithm
function	I-Algorithm
kernel	I-Algorithm
(	O
Gaussian	B-Algorithm
kernel	I-Algorithm
)	O
as	O
formulated	O
below	O
.	O
</s>
<s>
GRNN	O
has	O
been	O
implemented	O
in	O
many	O
computer	O
languages	O
including	O
MATLAB	B-Language
,	O
R	B-Language
-	I-Language
programming	I-Language
language	I-Language
,	O
Python	B-Language
(	O
programming	O
language	O
)	O
and	O
Node.js	B-Language
.	O
</s>
<s>
Neural	B-Architecture
networks	I-Architecture
(	O
specifically	O
Multi-layer	O
Perceptron	O
)	O
can	O
delineate	O
non-linear	O
patterns	O
in	O
data	O
by	O
combining	O
with	O
generalized	O
linear	O
models	O
by	O
considering	O
distribution	O
of	O
outcomes	O
(	O
sightly	O
different	O
from	O
original	O
GRNN	O
)	O
.	O
</s>
<s>
Single-pass	O
learning	O
so	O
no	O
backpropagation	B-Algorithm
is	O
required	O
.	O
</s>
