最終更新日：2024/08/08
(probability theory, machine learning) An algorithm that allocates a fixed limited set of resources between competing alternative choices so as to maximize the expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or allocations are made.
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multi-armed bandit
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元となった辞書の項目
multi-armed bandit
noun
(probability
theory,
machine
learning)
An
algorithm
that
allocates
a
fixed
limited
set
of
resources
between
competing
alternative
choices
so
as
to
maximize
the
expected
gain,
when
each
choice's
properties
are
only
partially
known
at
the
time
of
allocation,
and
may
become
better
understood
as
time
passes
or
allocations
are
made.
意味（1）
(probability
theory,
machine
learning)
An
algorithm
that
allocates
a
fixed
limited
set
of
resources
between
competing
alternative
choices
so
as
to
maximize
the
expected
gain,
when
each
choice's
properties
are
only
partially
known
at
the
time
of
allocation,
and
may
become
better
understood
as
time
passes
or
allocations
are
made.
