<s>
The	O
randomized	B-Algorithm
weighted	I-Algorithm
majority	I-Algorithm
algorithm	I-Algorithm
is	O
an	O
algorithm	O
in	O
machine	O
learning	O
theory	O
.	O
</s>
<s>
It	O
improves	O
the	O
mistake	O
bound	O
of	O
the	O
weighted	B-Algorithm
majority	I-Algorithm
algorithm	I-Algorithm
.	O
</s>
<s>
In	O
machine	O
learning	O
,	O
the	O
weighted	B-Algorithm
majority	I-Algorithm
algorithm	I-Algorithm
(	O
WMA	O
)	O
is	O
a	O
meta-learning	O
algorithm	O
which	O
"	O
predicts	O
from	O
expert	O
advice	O
"	O
.	O
</s>
<s>
The	O
weighted	B-Algorithm
majority	I-Algorithm
algorithm	I-Algorithm
(	O
WMA	O
)	O
makes	O
at	O
most	O
mistakes	O
,	O
</s>
<s>
The	O
Randomized	B-Algorithm
Weighted	I-Algorithm
Majority	I-Algorithm
Algorithm	I-Algorithm
can	O
be	O
used	O
to	O
combine	O
multiple	O
algorithms	O
in	O
which	O
case	O
RWMA	O
can	O
be	O
expected	O
to	O
perform	O
nearly	O
as	O
well	O
as	O
the	O
best	O
of	O
the	O
original	O
algorithms	O
in	O
hindsight	O
.	O
</s>
<s>
Furthermore	O
,	O
one	O
can	O
apply	O
the	O
Randomized	B-Algorithm
Weighted	I-Algorithm
Majority	I-Algorithm
Algorithm	I-Algorithm
in	O
situations	O
where	O
experts	O
are	O
making	O
choices	O
that	O
cannot	O
be	O
combined	O
(	O
or	O
ca	O
n't	O
be	O
combined	O
easily	O
)	O
.	O
</s>
