A
self-‐learning
Audio
Player
was
built
to
learn
users
habits
by
analyzing
operations
the
user
does
when
listening
to
music.
The
self-‐learning
component
is
intended
to
provide
a
better
music
experience
for
the
user
by
generating
a
special
playlist
based
on
the
prediction
of
users
favorite
songs.
The
rough
set
core
characteristics
are
used
throughout
the
learning
process
to
capture
the
dynamics
of
changing
user
interactions
with
the
audio
player.
The
engine
is
evaluated
by
simulation
data.
The
simulation
process
ensures
the
data
contain
specific
predetermined
patterns.
Evaluation
results
show
the
predictive
power
and
stability
of
the
hybrid
engine
for
learning
a
users
habits
and
the
increased
intelligence
achieved
by
combining
rough
sets
and
NN
when
compared
with
using
NN
by
itself.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OSUL.10219/2117 |
| Date | 16 October 2013 |
| Creators | Zuo, Hongming |
| Publisher | Laurentian University of Sudbury |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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