ECE 720 PROJECT
Winter 2017
Deadline: The report to be submitted on the last day of classes of the Winter semester.
Consider one-dimensional time series coming from the UCI machine learning repository
http://archive.ics.uci.edu/ml/
or KEEL
http://sci2s.ugr.es/keel/datasets.php
The time series under discussion belong to one of several classes. The data set is split into training and testing set (60-40). The training data are used to develop a classifier.
The series is quantized in amplitude space by using
(a) equal length intervals spread between min and max, and
(b) equal probability intervals
正在讨论的时间序列属于几类之一。数据集分为训练和测试集（60-40）。训练数据用于开发分类器。
该系列通过使用在幅度空间中量化
（a）在最小和最大之间的相等的长度间隔，以及
（b）等概率间隔
As a result, the series is represented as a sequence of visible symbols (states), say ABCCDFA…
因此，该系列被表示为可见符号（状态）的序列，例如ABCCDFA …
Design HMMs for this classification problem. Assume that each sequence starts with a null symbol and ends with an end null symbol. Present a thorough analysis of the obtained HMMs and analyze its performance. In particular, show-transition matrices
为此分类问题设计HMM。假设每个序列以空符号开始，并以结尾的空符号结束。对所获得的HMM进行全面分析并分析其性能。特别是显示
过渡矩阵
-analyze an impact of the number of hidden states and the number of symbols on the performance of the classifier – 分析隐藏状态数量和符号数量对分类器性能的影响
-classification performance obtained on the training and testing data
– 对培训和测试数据获得的分类表现
In your report include a code along with its detailed commenting. Describe the data set used.在您的报告中，包括代码以及详细的评论。描述使用的数据集。
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ECE
720
PROJECT
Winter
2017
Deadline:
The
report
to
be
submitted
on
the
last
day
of
classes
of
the
Winter
semester.
Consider
one-dimensional
time
series
coming
from
the
UCI
machine
learning
repository
http://archive.ics.uci.edu/ml/
or
KEEL
http://sci2s.ugr.es/keel/datasets.php
The
time
series
under
discussion
belong
to
one
of
several
classes.
The
data
set
is
split
into
training
and
testing
set
(60-40).
The
training
data
are
used
to
develop
a
classifier.
The
series
is
quantized
in
amplitude
space
by
using
(a)
equal
length
intervals
spread
between
min
and
max,
and
(b)
equal
probability
intervals
ÕýÔÚÌÖÂÛµÄʱ¼äÐòÁÐÊôÓÚ¼¸ÀàÖ®Ò»¡£Êý¾Ý¼¯·ÖΪѵÁ·ºÍ²âÊÔ¼¯£¨
60-40
£©¡£ÑµÁ·Êý
¾ÝÓÃÓÚ¿ª·¢·ÖÀàÆ÷¡£
¸ÃϵÁÐͨ¹ýʹÓÃÔÚ·ù¶È¿Õ¼äÖÐÁ¿»¯
£¨
a
£©ÔÚ×îСºÍ×î´óÖ®¼äµÄÏàµÈµÄ³¤¶È¼ä¸ô£¬ÒÔ¼°
£¨
b
£©µÈ¸ÅÂʼä¸ô
As
a
result,
the
series
is
represented
as
a
sequence
of
visible
symbols
(states),
say
ABCCDFA
…
Òò´Ë£¬¸ÃϵÁб»±íʾΪ¿É¼û·ûºÅ£¨×´Ì¬£©µÄÐòÁУ¬ÀýÈç
ABCCDFA
…
Design
HMMs
for
this
classification
problem.
Assume
that
each
sequence
starts
with
a
null
symbol
and
ends
with
an
end
null
symbol.
Present
a
thorough
analysis
of
the
obtained
HMMs
and
analyze
its
performance.
In
particular,
show-transition
matrices
Ϊ´Ë·ÖÀàÎÊÌâÉè¼Æ
HMM
¡£¼ÙÉèÿ¸öÐòÁÐÒÔ¿Õ·ûºÅ¿ªÊ¼£¬²¢ÒÔ½áβµÄ¿Õ·ûºÅ½áÊø¡£¶Ô
Ëù»ñµÃµÄ
HMM
½øÐÐÈ«Ãæ·ÖÎö²¢·ÖÎöÆäÐÔÄÜ¡£ÌرðÊÇÏÔʾ
¹ý¶É¾ØÕó
-analyze
an
impact
of
the
number
of
hidden
states
and
the
number
of
symbols
on
the
performance
of
the
classifier
–
·ÖÎöÒþ²Ø״̬ÊýÁ¿ºÍ·ûºÅÊýÁ¿¶Ô·ÖÀàÆ÷ÐÔÄܵÄÓ°Ïì
-classification
performance
obtained
on
the
training
and
testing
data
–
¶ÔÅàѵºÍ²âÊÔÊý¾Ý»ñµÃµÄ·ÖÀà±íÏÖ
In
your
report
include
a
code
along
with
its
detailed
commenting.
Describe
the
data
set
used.
ÔÚÄúµÄ±¨¸æÖУ¬°üÀ¨´úÂëÒÔ¼°ÏêϸµÄÆÀÂÛ¡£ÃèÊöʹÓõÄÊý¾Ý¼¯¡£