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APPENDIX
Solving the Intermediate Optimization
Problem
The optimization problem (21) for fixed w satisfying
the subnormalization constraints and given training
set T of N samples can be rewritten as
minimize
γ,ε
1
,...,ε
N
1
2γ
2
+ B
N
∑
n=1
ε
n
(23)
s.t. ex
n,c
≥ γ − ε
n
, ∀n and c 6= c
(n)
γ ≥ 0, ε
n
≥ 0, ∀n,
where we set ex
n,c
= ∆
n,c
w. For n = 1, . . . , N let
x
n
= min
c∈C ,c6=c
(n)
ex
n,c
.
Then, the above problem is equivalent to
minimize
γ,ε
1
,...,ε
N
1
2γ
2
+ B
N
∑
n=1
ε
n
(24)
s.t. x
n
≥ γ − ε
n
, ∀n
γ ≥ 0, ε
n
≥ 0, ∀n,
because the removed constraints will be simultane-
ously satisfied. In an optimal solution with margin γ
′
the term
∑
N
n=1
ε
n
must be as small as possible. There-
fore, all the ε
n
are required to take the minimal value
that is still feasible. This is ε
n
= γ
′
− x
n
, if this quan-
tity is positive. Or ε
n
= 0 otherwise. In this way, the
optimization problem becomes
minimize
γ
1
2γ
2
+ B
N
∑
n=1
max{γ− x
n
, 0} (25)
s.t. γ ≥ 0
and can be easily solved. If required, the slacks ε
n
can
subsequently be calculated as ε
n
= max{γ− x
n
, 0}.
CONVEX COMBINATIONS OF MAXIMUM MARGIN BAYESIAN NETWORK CLASSIFIERS
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