Q-Learning Support-Vector-Machine NIKKEI NET Infoseek MSN i
|
|
|
- みそら いくのや
- 7 years ago
- Views:
Transcription
1 21 Stock price forecast using text mining
2 Q-Learning Support-Vector-Machine NIKKEI NET Infoseek MSN i
3 Abstract Stock price forecast using text mining Koji Nakaya The stock prices forecast was done by the numeric character data.. However, Fund manager is having dealings over the stocks by using information in not only the numeric character data but also a qualitative news article. Then, text mining was used aiming to forecast stock price that used the quantitative data and qualitative data in this research. Qualitative data extracts the word with the possibility of influencing stock prices by using the article that relates to the economy of NIKKEI NET, Infoseek, and MSN Sankei news from October 1st to December 22nd by 170 articles and makes word vector of 121 words. The quantitative data uses the longitudinal data of Nikkei average closing share price from September 15th to December 22nd the tenth ago from the article announcement. Text mining by the Neural Network is used for the stock prices forecast. The learning technique of Neural Network uses Back Propagation. Verification data uses the longitudinal data of word vector and stock prices of 85 sets. Whether the teacher data is a rise of next day s stock prices is assumed to be binary of the descent. Data for the verification uses the longitudinal data of word vector and stock prices of the remainder of 85 sets. Only using together and word vector of word vector and stock prices compared the recognition rates of the longitudinal data of stock prices. The results show that using of word vector and stock prices together became the highest result. key words Neural Network, stock prices forecast, word vector ii
4 A 24 iii
5 A A A A A A A A A A iv
6 A A A A A A A A v
7 vi
8 1 90 [1] [2] ICT [2]
![[2] 1.](/docs-images/49/20192912/images/page_8.jpg "1 2 3 4 1")
9 1.1 2
10 2 MeCab
11 [3] 4
12 [3] [4] 5
13 ( 2.4) ( 2.5)
14 [5]
15 purelin(n) = n logsig(n) = - 1 (1 + exp( n)) 8
16 n = 2 (1 + exp( 2n)) 1-9
17
18
19
20
21 3 3.1 NIKKEI NET Infoseek MSN NIKKEI NET Infoseek MSN
22 [1] e-10 : 1 : : 2 :
23
24
25
26 e
27 4.2 20
28 2 1 21
29
30 [1] : [2] UFJ : [3] George Chang Marcus J. Healey James A. M. Mchugh Jason T. L. Wang Web 2005 [4] / 2006 [5],
31 A A
32 A
33 A
34 A
35 A
36 A
37 A
38 A
39 A
40 A
41 A
42 A
43 A
44 A
45 A
46 A
47 A
48 A
..,,,, , ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i
25 Feature Selection for Prediction of Stock Price Time Series 1140357 2014 2 28 ..,,,,. 2013 1 1 12 31, ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i Abstract Feature Selection for Prediction of Stock Price Time
Takens / / 1989/1/1 2009/9/ /1/1 2009/9/ /1/1 2009/9/30,,, i
21 Market forecast using chaos theory 1100334 2010 3 1 Takens / / 1989/1/1 2009/9/30 1997/1/1 2009/9/30 1999/1/1 2009/9/30,,, i Abstract Market forecast using chaos theory Hiroki Hara The longitudinal
29 Short-time prediction of time series data for binary option trade
29 Short-time prediction of time series data for binary option trade 1180365 2018 2 28 RSI(Relative Strength Index) 3 USD/JPY 1 2001 1 2 4 10 2017 12 29 17 00 1 high low i Abstract Short-time prediction
29 jjencode JavaScript
Kochi University of Technology Aca Title jjencode で難読化された JavaScript の検知 Author(s) 中村, 弘亮 Citation Date of 2018-03 issue URL http://hdl.handle.net/10173/1975 Rights Text version author Kochi, JAPAN http://kutarr.lib.kochi-tech.ac.jp/dspa
johnny-paper2nd.dvi
13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which
i
14 i ii iii iv v vi 14 13 86 13 12 28 14 16 14 15 31 (1) 13 12 28 20 (2) (3) 2 (4) (5) 14 14 50 48 3 11 11 22 14 15 10 14 20 21 20 (1) 14 (2) 14 4 (3) (4) (5) 12 12 (6) 14 15 5 6 7 8 9 10 7
22 Google Trends Estimation of Stock Dealing Timing using Google Trends
22 Google Trends Estimation of Stock Dealing Timing using Google Trends 1135064 3 1 Google Trends Google Trends Google Google Google Trends Google Trends 2006 Google Google Trend i Abstract Estimation
25 Removal of the fricative sounds that occur in the electronic stethoscope
25 Removal of the fricative sounds that occur in the electronic stethoscope 1140311 2014 3 7 ,.,.,.,.,.,.,.,,.,.,.,.,,. i Abstract Removal of the fricative sounds that occur in the electronic stethoscope
kut-paper-template.dvi
26 Discrimination of abnormal breath sound by using the features of breath sound 1150313 ,,,,,,,,,,,,, i Abstract Discrimination of abnormal breath sound by using the features of breath sound SATO Ryo
Web Web Web Web Web, i
22 Web Research of a Web search support system based on individual sensitivity 1135117 2011 2 14 Web Web Web Web Web, i Abstract Research of a Web search support system based on individual sensitivity
28 Docker Design and Implementation of Program Evaluation System Using Docker Virtualized Environment
28 Docker Design and Implementation of Program Evaluation System Using Docker Virtualized Environment 1170288 2017 2 28 Docker,.,,.,,.,,.,. Docker.,..,., Web, Web.,.,.,, CPU,,. i ., OS..,, OS, VirtualBox,.,
2 10 The Bulletin of Meiji University of Integrative Medicine 1,2 II 1 Web PubMed elbow pain baseball elbow little leaguer s elbow acupun
10 1-14 2014 1 2 3 4 2 1 2 3 4 Web PubMed elbow pain baseball elbow little leaguer s elbow acupuncture electric acupuncture 2003 2012 10 39 32 Web PubMed Key words growth stage elbow pain baseball elbow
第122号.indd
-1- -2- -3- 0852-36-5150 0852-36-5163-4- -5- -6- -7- 1st 1-1 1-2 1-3 1-4 1-5 -8- 2nd M2 E2 D2 J2 C2-9- 3rd M3 E3 D3 J3 C3-10- 4th M4 E4 D4 J4 C4-11- -12- M5 E5 J5 D5 C5 5th -13- -14- NEWS NEWS -15- NEWS
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
NEXT FUNDS NASDAQ-100 連動型上場投信
NEXT FUNDS NASDAQ-100 連動型上場投信 TOPIX Exchange Traded Fund Year Index Value Daily NAV Daily Return Unit NAV -Month Day Return (per Unit) Return Differential Outstanding (i) The TOPIX Index Value and the
SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i
20 SOM Development of Syllabus Vsualization System using Spherical Self-Organizing Maps 1090366 2009 3 5 SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i Abstract Development of Syllabus Vsualization
i ii iii iv v vi vii ( ー ー ) ( ) ( ) ( ) ( ) ー ( ) ( ) ー ー ( ) ( ) ( ) ( ) ( ) 13 202 24122783 3622316 (1) (2) (3) (4) 2483 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) 11 11 2483 13
262014 3 1 1 6 3 2 198810 2/ 198810 2 1 3 4 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1... 1... 2... 2... 4... 5... 9... 9... 10... 10... 10... 10... 13 2... 13 3... 15... 15... 15... 16 4...
1 1 tf-idf tf-idf i
14 A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles 1055104 2003 1 31 1 1 tf-idf tf-idf i Abstract A Method of Article Retrieval Utilizing Characteristics in Newspaper Articles
7,, i
23 Research of the authentication method on the two dimensional code 1145111 2012 2 13 7,, i Abstract Research of the authentication method on the two dimensional code Karita Koichiro Recently, the two
14 CRT Color Constancy in the Conditions of Dierent Cone Adaptation in a CRT Display
14 CRT Color Constancy in the Conditions of Dierent Cone Adaptation in a CRT Display 1030281 2003 2 12 CRT [1] CRT. CRT von Kries PC CRT CRT 9300K CRT 6500K CRT CRT 9300K x y S L-2M x y von Kries S L-2M
Web Web Web Web i
28 Research of password manager using pattern lock and user certificate 1170369 2017 2 28 Web Web Web Web i Abstract Research of password manager using pattern lock and user certificate Takuya Mimoto In
178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21
I 178 II 180 III ( ) 181 IV 183 V 185 VI 186 178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21 4 10 (
44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2)
(1) I 44 II 45 III 47 IV 52 44 4 I (1) ( ) 1945 8 9 (10 15 ) ( 17 ) ( 3 1 ) (2) 45 II 1 (3) 511 ( 451 1 ) ( ) 365 1 2 512 1 2 365 1 2 363 2 ( ) 3 ( ) ( 451 2 ( 314 1 ) ( 339 1 4 ) 337 2 3 ) 363 (4) 46
i ii i iii iv 1 3 3 10 14 17 17 18 22 23 28 29 31 36 37 39 40 43 48 59 70 75 75 77 90 95 102 107 109 110 118 125 128 130 132 134 48 43 43 51 52 61 61 64 62 124 70 58 3 10 17 29 78 82 85 102 95 109 iii
,,.,.,,.,.,.,.,,.,..,,,, i
22 A person recognition using color information 1110372 2011 2 13 ,,.,.,,.,.,.,.,,.,..,,,, i Abstract A person recognition using color information Tatsumo HOJI Recently, for the purpose of collection of
2 ( ) i
25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA
kut-paper-template2.dvi
19 A Proposal of Text Classification using Formal Concept Analysis 1080418 2008 3 7 ( ) Hasse Web Reuters 21578 Concept Explorer 2 4 said i Abstract A Proposal of Text Classification using Formal Concept
IT,, i
22 Retrieval support system using bookmarks that are shared in an organization 1110250 2011 3 17 IT,, i Abstract Retrieval support system using bookmarks that are shared in an organization Yoshihiko Komaki
kut-paper-template.dvi
14 Application of Automatic Text Summarization for Question Answering System 1030260 2003 2 12 Prassie Posum Prassie Prassie i Abstract Application of Automatic Text Summarization for Question Answering
(2003)
A discussion of research with questionnaires as psychological study through a comparison with clinical interview MOTONAGA, Takuro This study discusses underlying issues and viewpoints to administrate researches
Virtual Window System Virtual Window System Virtual Window System Virtual Window System Virtual Window System Virtual Window System Social Networking
23 An attribute expression of the virtual window system communicators 1120265 2012 3 1 Virtual Window System Virtual Window System Virtual Window System Virtual Window System Virtual Window System Virtual
soturon.dvi
12 Exploration Method of Various Routes with Genetic Algorithm 1010369 2001 2 5 ( Genetic Algorithm: GA ) GA 2 3 Dijkstra Dijkstra i Abstract Exploration Method of Various Routes with Genetic Algorithm
B_01田中.indd
A Study on the Image of the City in Contemporary Korea: Democracy and Busan Satoru Tanaka Abstract Busan has been the biggest harbor city in Korea, and ranked as one of the centers in democratization history
2 1 ( ) 2 ( ) i
21 Perceptual relation bettween shadow, reflectance and luminance under aambiguous illuminations. 1100302 2010 3 1 2 1 ( ) 2 ( ) i Abstract Perceptual relation bettween shadow, reflectance and luminance
II
No. 19 January 19 2013 19 Regionalism at the 19 th National Assembly Elections Focusing on the Yeongnam and Honam Region Yasurou Mori As the biggest issue of contemporary politics at South Korea, there
The research,consciousness of the mother who used temporary child care at the day nursery MATSUOKA Tomoko, SAKURADANI Mariko Research for the mother who has the experience which temporary child care was
,,,,., C Java,,.,,.,., ,,.,, i
24 Development of the programming s learning tool for children be derived from maze 1130353 2013 3 1 ,,,,., C Java,,.,,.,., 1 6 1 2.,,.,, i Abstract Development of the programming s learning tool for children
untitled
i ii iii iv v 43 43 vi 43 vii T+1 T+2 1 viii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 a) ( ) b) ( ) 51
Web Basic Web SAS-2 Web SAS-2 i
19 Development of moving image delivery system for elementary school 1080337 2008 3 10 Web Basic Web SAS-2 Web SAS-2 i Abstract Development of moving image delivery system for elementary school Ayuko INOUE
AccessflÌfl—−ÇŠš1
ACCESS ACCESS i ii ACCESS iii iv ACCESS v vi ACCESS CONTENTS ACCESS CONTENTS ACCESS 1 ACCESS 1 2 ACCESS 3 1 4 ACCESS 5 1 6 ACCESS 7 1 8 9 ACCESS 10 1 ACCESS 11 1 12 ACCESS 13 1 14 ACCESS 15 1 v 16 ACCESS
2
1 2 3 4 5 6 7 8 9 10 I II III 11 IV 12 V 13 VI VII 14 VIII. 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 _ 33 _ 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 VII 51 52 53 54 55 56 57 58 59
SURF,,., 55%,.,., SURF(Speeded Up Robust Features), 4 (,,, ), SURF.,, 84%, 96%, 28%, 32%.,,,. SURF, i
24 SURF Recognition of Facial Expression Based on SURF 1130402 2013 3 1 SURF,,., 55%,.,., SURF(Speeded Up Robust Features), 4 (,,, ), SURF.,, 84%, 96%, 28%, 32%.,,,. SURF, i Abstract Recognition of Facial
PC PDA SMTP/POP3 1 POP3 SMTP MUA MUA MUA i
21 The private mailers synchronization operation for plural terminals 1125083 2010 3 1 PC PDA SMTP/POP3 1 POP3 SMTP MUA MUA MUA i Abstract The private mailers synchronization operation for plural terminals
SNS ( ) SNS(Social Networking Service) SNS SNS i
22 SNS Job-Hunting Activities situation Understanding Support System Using SNS 1110252 2011 03 01 SNS ( ) SNS(Social Networking Service) SNS SNS i Abstract Job-Hunting Activities situation Understanding
橡最新卒論
Research of improving of recognition ability in Face recognition system Abstract The age when baiometrics was used as a password came today. Because various baiometrics such as a voice, a fingerprint,
(VKIR) VKIR VKIR DCT (R) (G) (B) Ward DCT i
24 Region-Based Image Retrieval using Color Histogram Feature 1130340 2013 3 1 (VKIR) VKIR VKIR DCT (R) (G) (B) 64 64 Ward 20 1 20 1 20. 5 10 2 DCT i Abstract Region-Based Image Retrieval using Color Histogram
評論・社会科学 ８４号（よこ）（Ｐ）／３．金子
1 1 1 23 2 3 3 4 3 5 CP 1 CP 3 1 1 6 2 CP OS Windows Mac Mac Windows SafariWindows Internet Explorer 3 1 1 CP 2 2. 1 1CP MacProMacOS 10.4.7. 9177 J/A 20 2 Epson GT X 900 Canon ip 4300 Fujifilm FinePix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 () - 1 - - 2 - - 3 - - 4 - - 5 - 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
CONTENTS 3 8 10 12 14 15 16 17 18 19 28 29 30 Public relations brochure of Higashikawa 9 2016 September No.755 2
9 2016 September No.755 CONTENTS 3 8 10 12 14 15 16 17 18 19 28 29 30 Public relations brochure of Higashikawa 9 2016 September No.755 2 3 5 4 6 7 9 8 11 10 HIGASHIKAWA TOWN NEWS 12 13 DVD 14 Nature Column
SERPWatcher SERPWatcher SERP Watcher SERP Watcher,
SERPWatcher 112-8610 2-1-1 112-8610 2-1-1 229-8558 5-10-1 E-mail: nakabe@db.is.ocha.ac.jp, chiemi@is.ocha.ac.jp SERPWatcher SERP Watcher SERP Watcher, SERP Analysis of transition of ranking in SERP Watcher
社会学部紀要 １１７号☆／１．野瀬
October 2013 A 13 2 c 1 1979 A 13 2 b c 1 2012 9 160 2 33 2 OECD 1 2 35.3 OECD 34 29 70.0 50.426 64.7 OECD 30.0 2.2 OECD GDP 2 0.5 31 31 OECD 1.1 45 3 3 1 1 2 200 400 2 1 2 400 600 600 800 13 2 c 2013
2015 ( 27 ) RFID RF RFID, 2., 3., 4. i
2015 ( 27 ) 71143204 2015 ( 27 ) RFID RF 1 100 98 20 20 1 19 1. RFID, 2., 3., 4. i Abstract of Bachelor s Thesis Academic Year 2015 Smart Management System of Rechargeable Batteries with Automatic Identification
*.....J.....S.q..2013B_....
1 1 2 2 3 3 4 4 5 6 5 7 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
TF-IDF TDF-IDF TDF-IDF Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Sat
1 1 2 1. TF-IDF TDF-IDF TDF-IDF. 3 18 6 Extracting Impression of Sightseeing Spots from Blogs for Supporting Selection of Spots to Visit in Travel Satoshi Date, 1 Teruaki Kitasuka, 1 Tsuyoshi Itokawa 2
ron.dvi
12 Effect of occlusion and perception of shadow in depth perception caused by moving shadow. 1010361 2001 2 5 (Occlusion), i Abstract Effect of occlusion and perception of shadow in depth perception caused
% 95% 2002, 2004, Dunkel 1986, p.100 1
Blended Learning 要 旨 / Moodle Blended Learning Moodle キーワード:Blended Learning Moodle 1 2008 Moodle e Blended Learning 2009.. 1994 2005 1 2 93% 95% 2002, 2004, 2011 2011 1 Dunkel 1986, p.100 1 Blended Learning
<4D6963726F736F667420576F7264202D20955D985F81458ED089EF89C88A77313036816989BA816A32303133944E82568C8E825093FA8C8E88B18DE28FE396EC92E78ADB8E5289FC>
QDA 1 2 2 1 2 2 3 3 1 3 2 4 4 1 4 2 1 EBP Evidence Based Practice EBP 1 2013 6 27 2013 7 1 Agency for Health Care Policy and Research : AHCPR Gambrill 2006 McNeece & Thyer 2004 AHCPR Ia ba b 2006 KJ 2
86 7 I ( 13 ) II ( )
10 I 86 II 86 III 89 IV 92 V 2001 93 VI 95 86 7 I 2001 6 12 10 2001 ( 13 ) 10 66 2000 2001 4 100 1 3000 II 1988 1990 1991 ( ) 500 1994 2 87 1 1994 2 1000 1000 1000 2 1994 12 21 1000 700 5 800 ( 97 ) 1000