MACHINE LEARNING TECHNIQUES - LASA

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6.3 Perceptron
The earliest neural network models go back to 1940 with the McCulloch & Pitts perceptron. The
perceptron consists of a simple threshold unit taking binary inputs X = { x x }
r
1 ,..., n
. Its output y is
the product of its entries multiplied by a (1-dim) weight matrix W, modulated by a transfer function
or an activity function f.
n
⎛⎛ ⎞⎞
y= f ⎜⎜ w ⋅ x = f W⋅X
⎝⎝ i=
0 ⎠⎠
∑ i i⎟⎟
( )
(6.1)
where wxis the bias and is generally negative.
0 0
Figure 6-1: Perceptron with constant bias
Classical transfer functions are:
The linear function f( x)
The step function
= x
⎧⎧1 if x ≥ 0
f( x)
= ⎨⎨
⎩⎩0
if x < 0
© A.G.Billard 2004 – Last Update March 2011