Research presentation at sigse

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2. 1.
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3. RQ1: ( )
RQ2:
1.
3
[3]
[3] , , , Vol. 33,
No.2, 2018 3 , pp, 124-131.

4. 2. (1/4):
[2] I.Goodfellow, et al., Deep Lerning, MIT Press, 2016
4
[2]

5. ( )
2. (2/4):
[5, 6]
…
…
…
[5] J. Li, et al., Feature Selection: A Data Perspective,
ACM Computing Surveys, Vol. 50, No. 6, Dec. 2017, 45 pages.
[6] S. Ozdemir, et al., Feature Engineering Made Easy, Packt, 2018.
5

6. 6
2. (3/4):
[6]
[6] S. Ozdemir, et al., Feature Engineering Made Easy, Packt, 2018.

7. 2. (4/4): VGG16
[8] K. Simonyan, et al., Very Deep Convolutional Networks for Large-Scale Image
Recognition, Apr. 2015, http://arxiv.org/pdf/1409.1556.pdf.
VGG16
7
5
100
1000
VGG16 [8]

8. 3.
8
[1]
[2] [3] [4]
[7]
[9] [5]
[6]
[11]
[8]
[10]

9. 4. (1/5):
[1]
[2]
[3]
[6]
[4]
[11]
[5]
[8][9]
[10]
[7]
9

10. VGG16
4. (2/5):
10
[3]
[4]
VGG16
VGG16
1 1

11. 11
4. (3/5):
1:
2:
3:
…
( )
,

12. S ∝ ∝"#
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4. (4/5):
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/ = 0
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{3 ' − 4 ' }
6 7 :
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n:
<=: < >=: >
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13. 4. (5/5):
13
1
2
3
...
y y
n n
S< "# $ < $#

14. 5.
3
( ) LOC
Python(39) Python(138)
Python(137) Python(Chainer [7])(110)
(ChainerUI)
Python(33) Python(Chainer)
(ChainerUI)
(1)
(2)
(3)
[7] Preferred Networks, Chainer, https://chainer.org/. 14

15. 6.
15
(3 4,000 )

16. 2
(
! " = $. &"
1
1:600-699
2:700-799
3:800-899
94%
3
2
1:600-699
2:700-799
3:800-899
6
41:'(=30,)(=1.5
3:'(=10,)(=0.5
2:'(=20,)(=1.0
1:'(=30,)(=1.5
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600-899
100
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,
7.
16
94%
2
900(6 )

17. 5.4%
8. (1/4)
750 1050 1410 750 1050 1410
0.019 0.005 0.012 0.041 0.037 0.028 17
1
0.020

18. 150 300 450 600 750 900
0.839 0.891 0.940 0.954 0.952 0.981
0.895 0.897 0.928 0.945 0.941 0.964
8. (2/4)
18
1
2
3
4
1
2
3
4
< "/$ > "/&
2
=
( )** +,()**)
( "$* +,("$*)
M(n):
m(n):
n:
M(150)
m(150)
M(900)
m(900)
M(150)
m(150)
M(900)
m(900)

19. 19
8. (3/4)
2 S
S
S S
! = #
$
%
{' ( − * ( }
, - :
/ - :
n:
750,900
150,300,450,600
150,300,450,600
750,900

20. 20
8. (4/4)
2 S
=
" #$$ %&(#$$)
" )*$ %&()*$)
M(n):
m(n):
n:
1
2
3
4
1
2
3
4
< )/- > )//
S
M(150)
m(150)
M(900)
m(900)
M(150)
m(150)
M(900)
m(900)

21. RQ1:
RQ2:
21
9.
⇒
⇒
( )

22. 10.
22
RNN

23. 23
11.
RQ1:
RQ2:
S

24. 24
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END