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affect the output sign of the classifier but only its magnitude. For this purpose, we
propose the use of a new vector ʲ which is defined as:
2 k
1
ʲ i
= ʱ i
,
(5.1)
C
where k is the number of bits. Moreover, by omitting the equality constraint of the
dual SVM formulation ( y T
0) on Eq. ( 2.13 ) , we can search for an SVM solution
without a bias term b . This is clearly advantageous when we deal with a fixed-point
arithmetic formulation as this value can be difficult to control and instead the FFP
can be easily computed as:
ʱ =
n
f
(
x
) =
y i ʱ i K
(
x i ,
x
)
(5.2)
i
=
1
This modification has no influence on the classification performance of the trained
model as far as a Radial Basis Function (RBF) kernel, such as the Gaussian or the
Laplacian ones, is exploited (Poggio et al. 2002 ). These two modifications yield the
following formulation:
2 k
1
2 ʲ
1
T Q ʲ
s T
min
ʲ
ʲ
s.t. 0
ʲ i
i
∈ {
1
, ...,
n
} ,
(5.3)
C
= 2 k
1 /
where s i
. Once the problem expressed in Eq. ( 5.3 )
is solved, ʲ can straightforwardly target fixed-point arithmetic through a simple
nearest-integer normalization (Anguita et al. 2007 ).
To finally have a full FFP with only integer values, it is needed to modify the
representation of the kernel K
C
i
∈ {
1
, ...,
n
}
( · , · )
and the input vector x in terms of number of bits
( u and
v
bits respectively) (Anguita et al. 2007 ). This produces:
2 u
0
K
(
x i ,
x
)
1
i
∈ {
1
, ...,
n
} ,
(5.4)
2 v
0
x j
1
j
∈ {
1
, ...,
d
} .
(5.5)
Consequently the modified Fixed-Point FFP formulation vector is:
n
f
(
x
) =
y i ʲ i K
(
x i ,
x
)
(5.6)
i
=
1
In particular, we opted for a Laplacian kernel ( K x i ,
x j =
1 ), instead
of the more conventional Gaussian kernel, as it is more convenient for hardware lim-
ited devices (Anguita et al. 2007 ) because it can be easily computed using shifters.
The Manhattan norm is defined as
2 ʳ
x i
x j
j = 1 x j and
x 1
=
ʳ >
0 is the kernel
hyperparameter which can be selected (altogether with the regularization hyperpa-
rameter C ), for example, through a k -Fold Cross Validation (KCV) procedure (with
 
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