Similar documents
情報処理学会研究報告 IPSJ SIG Technical Report Vol.2015-DBS-162 No /11/26 1,a) 1,b) EM Designing and developing an interactive data minig tool for rapid r

_314I01BM浅谷2.indd

untitled

x T = (x 1,, x M ) x T x M K C 1,, C K 22 x w y 1: 2 2

21 Pitman-Yor Pitman- Yor [7] n -gram W w n-gram G Pitman-Yor P Y (d, θ, G 0 ) (1) G P Y (d, θ, G 0 ) (1) Pitman-Yor d, θ, G 0 d 0 d 1 θ Pitman-Yor G

motomura.dvi

Microsoft PowerPoint - SSII_harada pptx

IPSJ SIG Technical Report Vol.2013-ICS-172 No /11/12 1,a), 1,b) Anomaly Detection 1. 1 Nagoya Institute of Technology 1 Presently with Nagoya In

3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)

No. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1

独立行政法人情報通信研究機構 Development of the Information Analysis System WISDOM KIDAWARA Yutaka NICT Knowledge Clustered Group researched and developed the infor

aca-mk23.dvi

2. Twitter Twitter 2.1 Twitter Twitter( ) Twitter Twitter ( 1 ) RT ReTweet RT ReTweet RT ( 2 ) URL Twitter Twitter 140 URL URL URL 140 URL URL

& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),

b n m, m m, b n 3

Duality in Bayesian prediction and its implication

Kobe University Repository : Kernel タイトル Title 著者 Author(s) 掲載誌 巻号 ページ Citation 刊行日 Issue date 資源タイプ Resource Type 版区分 Resource Version 権利 Rights DOI

ISCO自動コーディングシステムの分類精度向上に向けて―SSM およびJGSS データセットによる実験の結果―

,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3

20mm 63.92% ConstantZoom U 5

要旨 1. 始めに PCA 2. 不偏分散, 分散, 共分散 N N 49

ii

WHITE PAPER RNN

カルマンフィルターによるベータ推定( )

X X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I


2016 Institute of Statistical Research

1.0, λ. Holt-Winters t + h,ỹ t ỹ t+h t = ỹ t + hf t.,,.,,,., Hassan [5],,,.,,,,,,Hassan EM,, [6] [8].,,,,Stenger [9]. Baum-Welch, Baum-Welch (Incremen

わが国企業による資金調達方法の選択問題

untitled

johnny-paper2nd.dvi


Research on decision making in multi-player games with imperfect information

IPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and

,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw

H22H23 4.

知能と情報, Vol.29, No.6, pp

GUI(Graphical User Interface) GUI CLI(Command Line Interface) GUI

Dirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :

三石貴志.indd

Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution

? (EM),, EM? (, 2004/ 2002) von Mises-Fisher ( 2004) HMM (MacKay 1997) LDA (Blei et al. 2001) PCFG ( 2004)... Variational Bayesian methods for Natural

IPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan

まちno10.indd

Vol. 23 No. 4 Oct Kitchen of the Future 1 Kitchen of the Future 1 1 Kitchen of the Future LCD [7], [8] (Kitchen of the Future ) WWW [7], [3

IPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie

Honda 3) Fujii 4) 5) Agrawala 6) Osaragi 7) Grabler 8) Web Web c 2010 Information Processing Society of Japan

シリコンバレーとルート128における地域産業システムのその後の展開―経営学輪講 Saxenian (1994)

IPSJ SIG Technical Report Vol.2014-DBS-159 No.6 Vol.2014-IFAT-115 No /8/1 1,a) 1 1 1,, 1. ([1]) ([2], [3]) A B 1 ([4]) 1 Graduate School of Info


2 A A 3 A 2. A [2] A A A A 4 [3]

34 (2017 ) Advances in machine learning technologies make inductive programming a reality. As opposed to the conventional (deductive) programming, the

Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho

橡表紙参照.PDF

00hyoshi

フリーソフトではじめる機械学習入門 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.

*2.5mm ”ŒŠá‡ÆfiÁ™¥‡Ì…Z†[…t…X…N…−†[…j…fi…O

HASC2012corpus HASC Challenge 2010,2011 HASC2011corpus( 116, 4898), HASC2012corpus( 136, 7668) HASC2012corpus HASC2012corpus

Vol.55 No (Jan. 2014) saccess 6 saccess 7 saccess 2. [3] p.33 * B (A) (B) (C) (D) (E) (F) *1 [3], [4] Web PDF a m

調査資料 -253 国際 国内会議録の簡易分析に基づく 我が国の人工知能研究動向把握の試み 2016 年 8 月 文部科学省科学技術 学術政策研究所 科学技術予測センター 小柴等


ナI.pdf


ばらつき抑制のための確率最適制御

住宅(本文、図表)

fiš„v8.dvi

Corporate Principle

01.trtitle.doc

1 2


MPC MPC R p N p Z p p N (m, σ 2 ) m σ 2 floor( ), rem(v 1 v 2 ) v 1 v 2 r p e u[k] x[k] Σ x[k] Σ 2 L 0 Σ x[k + 1] = x[k] + u[k floor(l/h)] d[k]. Σ k x

,, WIX. 3. Web Index 3. 1 WIX WIX XML URL, 1., keyword, URL target., WIX, header,, WIX. 1 entry keyword 1 target 1 keyword target., entry, 1 1. WIX [2

ディープラーニングとオープンサイエンス ~研究の爆速化が引き起こす摩擦なき情報流通へのシフト~

untitled

untitled

量子情報科学−情報科学の物理限界への挑戦- 2018

Vol.20, No.1, 2018 Castillo [10] Yang [11] Sina Weibo 3 Castillo [10] Twitter 4 Twitter [12] Twitter ) 2 Twitter [13] 3. Twitter Twitter 3

16) 12) 14) n x i, (1 i < n) x 1 = x 2 = = x n. (6) L = D A (1) D = diag(d 1,d 2,,d n ) n n A d i = j i a i j 9) 0 a 12 a 13 a 14 A = a 21 0 a

2

GDP tax expenditure GDP GDP GDP TANF GDP

Q [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]


guideline_1_0.dvi


橡同居選択における所得の影響(DP原稿).PDF

[1] SBS [2] SBS Random Forests[3] Random Forests ii

fiš„v5.dvi

Ground Design for Creative Systems Theory proceeding verson MIT Center for Collective Intelligence 1 (Torvalds and Diamond, 2002; Gloor, 2006; Friedma

, vol.33, no.2, pp.86 91, Machine Learning with Mutual Information and Its Application in Robotics 1,2 2, Masashi Sugiyama 1,2, Kiyos

和文タイトル

人工知能と人間社会に関する検討の国内外の動向

Vol.58 No (Sep. 2017) 1 2,a) 3 1,b) , A EM A Latent Class Model to Analyze the Relationship Between Companies Appeal Poi


2 3 Fig. 2 3-layer structure model ( 3 ) Fig. 1 1 Period for increasing knowledge and familiarity ) 3 ( 2 ) ( 2(a)) 2 6 (

cover.ai

[2][3][4][5] 4 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 2. Shiratori [2] Shiratori [3] [4] GP [5] [6] [7] [8][9] Kinect Choi [10] 3. 1 c 2016 Information Processing So

:EM,,. 4 EM. EM Finch, (AIC)., ( ), ( ), Web,,.,., [1].,. 2010,,,, 5 [2]., 16,000.,..,,. (,, )..,,. (socio-dynamics) [3, 4]. Weidlich Haag.


IPSJ SIG Technical Report Vol.2014-MBL-70 No.49 Vol.2014-UBI-41 No /3/15 2,a) 2,b) 2,c) 2,d),e) WiFi WiFi WiFi 1. SNS GPS Twitter Facebook Twit

Vol.8 No (July 2015) 2/ [3] stratification / *1 2 J-REIT *2 *1 *2 J-REIT % J-REIT J-REIT 6 J-REIT J-REIT 10 J-REIT *3 J-

Transcription:

1 IDC Wo rldwide Business Analytics Technology and Services 2013-2017 Forecast 2 24 http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h24/pdf/n2010000.pdf 3 Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011. 4 25 http://datascientist.ism.ac.jp/pdf/h25dstn.pdf. 5 Fairness, Acountability, and Transparency in Machine Learning, http://www.fatml.org/index.html 6 Cynthia Rudin, Algorithm for interpretable machine learning, Invited Talk in 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD, 2014 7 Big Data: Seizing Opportunities, Preserving Values, Exective Office of the President White House, 2014, https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_ print.pdf 8 Vladimir N. Vapnik, The nature of statistical Learning Theory, Springer-Verlag New York, 1995. 9 Akaike, H., Information theory and an extension of the maximum likelihood principle Proceedings of the 2nd International Symposium on Information Theory, 267-281, 1973 10 G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6 2 :461.464, 1978. 11 S. Watanabe, Algebraic Geometry and Statistical Learning Theory, Cambridge University Press, 2009 12 M. Lichman, UCI Machine Learning Repository, http://archive.ics.uci.edu/mi, 2013.

13 Ryohei Fujimaki, Yasuhiro Sogawa, Satoshi Morinaga: Online heterogeneous mixture modeling with marginal and copula selection. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD, 2011 14 Ryohei Fujimaki, Satoshi Morinaga: Factorized Asymptotic Bayesian Inference for Mixture Modeling. Proceedings of the The fifteenth international conference on Artificial Intelligence and Statistics AISTATS, 2012 15 Ryohei Fujimaki, Kohei Hayashi: Factorized Asymptotic Bayesian Hidden Markov Model. Proceedings of the 25th international conference on machine learning ICML, 2012 16 K. Hayashi and R. Fujimaki, "Factorized Asymptotic Bayesian Inference for Latent Feature Models", 27th Annual Conference on Neural Information Processing Systems NIPS, 2013. 17 Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano, Fully-Automatic Bayesian Piece-wise Sparse Linear Models, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics AISTATS, 2014 18 Ji Liu, Ryohei Fujimaki and Jieping Ye, "Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint", Proceedings of the 27th international conference on machine learning ICML, 2014 19 H. Oiwa and R. Fujimaki, "Partition-wise Linear Models", 28th Annual Conference on Neural Information Processing Systems NIPS, 2014. 20 NEC 2013 10 29 NEC http://jpn.nec.com/ press/201310/20131029_02.html 21 NEC - http://jpn.nec.com/bigdata/example/value.html 22 2015 2 16 7 NEC 23 NEC 2014 11 12 NEC NEC http://jpn.nec.com/press/201411/20141112_02.html