|
|
|
- ふじきみ なかじゅく
- 5 years ago
- Views:
Transcription
1 Introduction of Self-Organizing Map * 1 Ver ( ) *1 furukawa@brain.kyutech.ac.jp
2
3 i SOM SOM SOM SOM SOM
4
5 1 1 (Self-Organizing Map: SOM) T. Kohonen SOM (topology preserving mapping) [1, 2, 3] SOM 2 (topographic map) SOM SOM (self-organization) Kohonen SOM SOM (1) Winner-Take-All (2) (3) Hebb [4] EM E M [5] SOM (Principal Component Analysis: PCA) (Multidimensional Scaling: MDS) (minifold learning) SOM (Vector Quantization: VQ) SOM (latent variable model) (Generative Topographic Mapping: GTM)[6] (Gaussian Process Latent Variable Model: GPLVM)[7] SOM
6 SOM / *1 SOM topographic map, *1 [3] p.170 /
7 MDS SOM *2 1.1 U-matrix U-matrix U-matix?? (MDS) 1.2 *2 magnification factor
8 4 1 MDS SOM MDS 1.2 SOM 1980 [1] SOM (V1) V1 (retinotopy) [8] V1 [9] Kohonen Type-1 Type-2 [10] Kohonen SOM Type-2 SOM Self-organizing feature map (SOFM) Kohonen s map SOM SOM Winner-Take-All Hebb SOM SOM SOM SOM SOM [11]
9 1.3 5 [12, 13] 1990 SOM [14, 15, 5] SOM SOM SOM Self-Organizing Map *3 Self-Organizing Feature Map (SOFM) Kohonen map/net SOM 1.3 SOM SOM (observable space) (latent space) *4 (node) SOM SOM SOM X Z SOM f : Z X X = (x 1,..., x N ) x n f (z n ) f Z = (z 1,..., z N ) SOM k ζ k Z y k X {ζ 1,..., ζ K } f y k = f (ζ k ) f SOM f y k *3 Map (topographic map) (mathematical map) *4 SOM
10 SOM x n (Best Matching Unit: BMU) Heykin SOM (Competitive process), (Cooperative process), (Adaptive process) [4] Step 0 ( ): y k (0) t = 1 Step 1 Step 1 ( ): x n (n = 1,..., N) (Best Matching Unit: BMU) BMU kn x n n, kn(t) = arg min x n y k (t 1) 2 (1.1) k z n (t) BMU ζ k n (t) Step 2 ( ): R kn n, k R kn (t) = h(z n (t), ζ k ; σ(t)) (1.2) h(z, ζ) BMU [ h(z, ζ; σ) = exp 1 ] z ζ 2 2σ2 (1.3) σ(t) σ(t) t
11 1.3 7 Step 3 ( ): k, y k (t) = N n=1 R kn(t)x n N n =1 R kn (t) (1.4) t := t + 1 Step 1 z n SOM EM (Expectation Maximization algorithm: EM algorithm) [5] E M E, M X = (x 1,..., x N ) R D N *5 Y = (y 1,..., y K ) R D K B = ( ) δ k,k n δ kk H = (H kk ) R K K H kk = h(ζ k, ζ k ; σ(t)) (1.5) R R = (R kn ) = HB (1.6) G k = N n=1 R kn G 1 0 G = diag(g k ) =... 0 G K (1.7) Y T = G 1 RX = G 1 HBX T (1.8) SOM PCA z n (Step 2) PCA *5 D N N D X T
12 σ(t) t l σ 0 l/2 σ(t) σ min σ min σ min σ min l/10 τ τ σ(t) = max [σ 0 (1 t/τ), σ min ] (1.9) σ(t) = max [ σ 0 exp ( t/τ), σ min ] (1.10) max[a, b] a, b τ 1.4 [16] component plane U-matrix Component plane D d y kd component plane U-matrix [17] 1.1 U-matrix 1.5 SOM F = N n=1 k=1 K h(z n, ζ k ) x n y k 2 (1.11) F Y = (y k ) U = (u n ) EM [5] Y = (y k ) F z n z n = arg min ζ k K h(ζ k, ζ k ) x n y k 2 k =1
13 1.6 SOM 9 K h(ζ k, ζ k ) x n y k 2 x n y k 2 k =1 SOM (1.1) Z = (z n ) F y k F y k = N h(z n, ζ k )(x n y k ) = 0 (1.12) n=1 N n=1 y k = h(z n, ζ k )x n N n =1 h(z n, ζ k ) (1.13) (1.4) 1.6 SOM PCA MDS SOM (Principal curve) (Elastic net) Isometric feature mapping: ISOMAP Locally Linear Embedding (LLE) SOM SOM k-means Fuzzy k-means (Neural Gas: NG) SOM SOM Growing NG Evolving SOM (Gaussian Mixture Model: GMM) (Factor Analysis: FA) (Generative Topographic Mapping: GTM) [6] (Gaussian Process Latent Variable Model: GPLVM) [7, 18] SOM
14 SOM SOM PAK [19] MATLAB SOM Toolbox [20] SOM U-matrix [2] [3] [11] SOM [12, 13]
15 11 2 SOM SOM (1) SOM (2) (3) (4) SOM (5) (6) SOM (7) SOM SOM 2 SOM SOM
16 12 2 SOM 1 SOM SOM SOM 1 SOM 2 2 SOM SOM SOM BMI SOM kg SOM 1
17 SOM 100m SOM SOM SOM SOM (outlier) t SOM σ 0 σ mi τ
18 14 2 SOM σ 0 σ min σ min τ τ σ 0, σ min, τ SOM (1.11) (1) (2) τ (3) (1), (2) (1) (2) τ (3) (1.11) F F SOM
19 2.2 SOM PCA 3 PCA X R D N (Singular Value Decomposition: SVD) X = UΣV T (2.1) U R D I, V R N I Σ R I I σ 1,..., σ I σ i σ 1 σ 2 σ I U = (u 1,..., u I ) u i i U 123 = (u 1, u 2, u 3 ) 3 x 123 = U T 123 x (2.2) U 124 U Ẑ ΣV T, Ẑ = (ẑ 1,..., ẑ N ) ẑ n z n PCA 2.2.3
20 16 2 SOM SOM 1 PCA SOM (1.11) F PCA MDS 2.3 SOM SOM SOM
21 17 3 SOM SOM 3.1 SOM SOM N K D N K D exp N K SOM O(NKD) N D N 3.2 N 1 ND O(NKD) Algorithm 1 O(NKD) N K Algorithm 2 O(K 2 D) *1 Y T = G 1 (HB)X T Y T = G 1 H(BX T ) B 0 1 BX T 0 3σ 0 2σ *1 N > K O(K 2 D) N < K O(NKD) N K
22 18 3 SOM Algorithm 1 Cooperative and Adaptive process (1) for n = 1 to N do for k = 1 to K do R kn neighborhood(k n, k, σ) end for end for for k = 1 to K do G k N n=1 R kn for d = 1 to D do Y kd N n=1 R knx kd Y kd Y kd /G k end for end for Algorithm 2 Cooperative and Adaptive process (2) for k = 1 to K do for k = 1 to K do H kk neighborhood(k, k, σ) end for end for k, ŷ k 0, N k 0, G k 0 for n = 1 to N do ŷ k n ŷ k n + x n N k n N k n + 1 end for for k = 1 to K do for k = 1 to K do if N k 0 then G k G k + N k R kk y k y k + H kk ŷ k end if end for y k y k /G k end for 1
23 D SOM O(NKD) (1) K (2) N (3) D K SOM K σ 2σ * D D L 2 L 1 D *3 SOM N N < N *2 *3 Tensor SOM D
24 20 3 SOM N Y kd (t + 1) = R kn X n d (3.1) X n d N Y = G 1 HBX N Y kd (t + 1) = (1 ε)y kd (t) + ε R kn X n d (3.2) N n =1 n =1 3.4 τ PCA PCA N 3.5 PC Tensor SOM PC
25 21 [1] T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics 43 (1) (1982) [2] T. Kohonen, Self-Organizing Maps, Springer-Verlag, Berlin Heidelberg, [3] T.,,, [4] S. Haykin, Neural Networks and Learning Machines, Prentice Hall, 2009, Ch. 9, pp [5] J. Verbeek, N. Vlassis, B. Krose, Self-organizing mixture models, Neurocomputing 63 (2005) doi: /j.neucom [6] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, Ch. 12, pp [7] N. D. Lawrence, Gaussian process latent variable models for visualisation of high dimensional data, in: Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada], 2003, pp URL [8] S.-I. Amari, Topographic organization of nerve fields, Bulletin of Mathematical Biology 42 (3) (1980) doi: /bf [9] C. von der Malsburg, Self-organization of orientation sensitive cells in the striate cortex, Kybernetik 14 (2) (1973) doi: /bf [10] T. Kohonen, Self-organizing neural projections, Neural Networks 19 (6 7) (2006) [11] T. Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) [12] S. Kaski, J. Kangas, T. Kohonen, Bibliopgraphy of self-organizing map (SOM) papers: , Neural Computing Surveys 1 (1998) [13] M. Oja, S. Kaski, T. Kohonen, Bibliopgraphy of self-organizing map (SOM) papers: addendum, Neural Computing Surveys 3 (2003) [14] C. M. Bishop, M. Svensen, C. K. I. Williams, GTM: The generative topographic mapping, Neural Computation 10 (1998) [15] C. M. Bishop, M. Svensen, C. K. I. Williams, Developments of the generative topographic mapping., Neurocomputing 21 (1-3) (1998) [16] P. Stefanovic, O. Kurasova, Visual analysis of self-organzing maps, Nonlinear Analysis: Modelling and Control 16 (4) (2011) [17] A. Ultsch, H. P. Siemon, Kohonen s self organizing feature maps for exploratory data
26 22 analysis., in: Proc. INNC 90, Int. Neural Network Conf., 1990, pp [18] N. Lawrence, Probabilistic non-linear principal component analysis with gaussian process latent variable models, Journal of Machine Learning Research 6 (2005) 1783? [19] SOM PAK. URL lvq pak.shtml [20] SOM Toolbox. URL
Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution
Convolutional Neural Network 2014 3 A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi 1940 1980 [1] 90 3
TC1-31st Fuzzy System Symposium (Chofu, September -, 15) cremental Neural Networ (SOINN) [5] Enhanced SOINN (ESOINN) [] ESOINN GNG Deng Evolving Self-
![TC1-31st Fuzzy System Symposium (Chofu, September -, 15) cremental Neural Networ (SOINN) [5] Enhanced SOINN (ESOINN) [] ESOINN GNG Deng Evolving Self-](/thumbs/78/78095837.jpg "TC1-31st Fuzzy System Symposium (Chofu, September -, 15) cremental Neural Networ (SOINN) [5] Enhanced SOINN (ESOINN) [] ESOINN GNG Deng Evolving Self-")
TC1-31st Fuzzy System Symposium (Chofu, September -, 15) Proposing a Growing Self-Organizing Map Based on a Learning Theory of a Gaussian Mixture Model Kazuhiro Tounaga National Fisheries University Abstract:
情報化社会に関する全国調査中間報告書
9 1 1990 1998 25.2% 2000 38.6% 2001 50.1% 2002 3 57.2% 2001 12 60.5% 2002 3 49.5% 2001 12 44.0% 2002 1 1992 0 2 1993 1 2 1994 84 37 1995 467 283 1996 1411 1080 1997 1621 1057 1998 1700 1098 1999 3036 1666
18 2 20 W/C W/C W/C 4-4-1 0.05 1.0 1000 1. 1 1.1 1 1.2 3 2. 4 2.1 4 (1) 4 (2) 4 2.2 5 (1) 5 (2) 5 2.3 7 3. 8 3.1 8 3.2 ( ) 11 3.3 11 (1) 12 (2) 12 4. 14 4.1 14 4.2 14 (1) 15 (2) 16 (3) 17 4.3 17 5. 19
R¤Çʬ¤«¤ëÎÏ³Ø·Ï - ¡Áʬ´ô¤ÎÍͻҤò²Ä»ë²½¤·¤Æ¤ß¤ë¡Á
.... R 2009 3 1 ( ) R 2009 3 1 1 / 23 : ( )!, @tkf, id:tkf41, (id:artk ) : 4 1 : http://arataka.wordpress.com : Python, C/C++, PHP, Javascript R : / ( ) R 2009 3 1 2 / 23 R? R! ( ) R 2009 3 1 3 / 23 =
T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,
![T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,](/thumbs/93/114160334.jpg "T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,")
(ver. 4:. 2005-07-27) 1 1.1 (mixed matrix) (layered mixed matrix, LM-matrix) m n A = Q T (2m) (m n) ( ) ( ) Q I m Q à = = (1) T diag [t 1,, t m ] T rank à = m rank A (2) 1.2 [ ] B rank [B C] rank B rank
untitled
K-Means 1 5 2 K-Means 7 2.1 K-Means.............................. 7 2.2 K-Means.......................... 8 2.3................... 9 3 K-Means 11 3.1.................................. 11 3.2..................................
Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U
![Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U](/thumbs/101/150229680.jpg "Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U")
Computer Security Symposium 017 3-5 October 017 1,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) 1. 017 5 [1] 1 Meiji University Graduate School of Advanced Mathematical Science
fiš„v3.dvi
(2001) 49 1 23 42 2000 10 16 2001 4 23 NTT * 1. 1.1 1998 * 104 0033 1 21 2 7F 24 49 1 2001 1999 70 91 MIT M. Turk Recognition Using Eigenface (Turk and Pentland (1991)). 1998 IC 1 CPU (Jain and Waller
Microsoft PowerPoint - SSII_harada pptx
The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information
& 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),
.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 2 Hough Forest Hough Forest[6] Random Forest( [5]) Random Forest Hough Forest Hough Forest 2.1 Hough Forest 1 2.2
![IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 2 Hough Forest Hough Forest[6] Random Forest( [5]) Random Forest Hough Forest Hough Forest 2.1 Hough Forest 1 2.2](/thumbs/91/106196100.jpg "IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 2 Hough Forest Hough Forest[6] Random Forest( [5]) Random Forest Hough Forest Hough Forest 2.1 Hough Forest 1 2.2")
IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 MI-Hough Forest () E-mail: ym@vision.cs.chubu.ac.jphf@cs.chubu.ac.jp Abstract Hough Forest Random Forest MI-Hough Forest Multiple Instance Learning Bag Hough Forest
04-04 第 57 回土木計画学研究発表会 講演集 vs
04-04 vs. 1 2 1 980-8579 6-6-06 E-mail: shuhei.yamaguchi.p7@dc.tohoku.ac.jp 2 980-8579 6-6-06 E-mail: akamatsu@plan.civil.tohoku.ac.jp Fujita and Ogawa(1982) Fujita and Ogawa Key Words: agglomeration economy,
it-ken_open.key
深層学習技術の進展 ImageNet Classification 画像認識 音声認識 自然言語処理 機械翻訳 深層学習技術は これらの分野において 特に圧倒的な強みを見せている Figure (Left) Eight ILSVRC-2010 test Deep images and the cited4: from: ``ImageNet Classification with Networks et
…p…^†[…fiflF”¯ Pattern Recognition
Pattern Recognition Shin ichi Satoh National Institute of Informatics June 11, 2019 (Support Vector Machines) (Support Vector Machines: SVM) SVM Vladimir N. Vapnik and Alexey Ya. Chervonenkis 1963 SVM
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,
三石貴志.indd
流通科学大学論集 - 経済 情報 政策編 - 第 21 巻第 1 号,23-33(2012) SIRMs SIRMs Fuzzy fuzzyapproximate approximatereasoning reasoningusing using Lukasiewicz Łukasiewicz logical Logical operations Operations Takashi Mitsuishi
2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [
![2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [](/thumbs/93/111684107.jpg "2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [")
RI-002 Encoding-oriented video generation algorithm based on control with high temporal resolution Yukihiro BANDOH, Seishi TAKAMURA, Atsushi SHIMIZU 1 1T / CMOS [1] 4K (4096 2160 /) 900 Hz 50Hz,60Hz 240Hz
,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
untitled
2007 55 2 255 268 c 2007 2007 1 24 2007 10 30 k 10 200 11 110 6 60 3 1. 1 19 Mendenhall 1887 Dickens, 1812 1870 Thackeray, 1811 1863 Mill, 1806 1873 1960 610 0394 1 3 256 55 2 2007 Sebastiani 2002 k k
Overview (Gaussian Process) GPLVM GPDM 2 / 59
daichi@ism.ac.jp 2015-3-3( ) 1 / 59 Overview (Gaussian Process) GPLVM GPDM 2 / 59 (Gaussian Process) y 2 1 0 1 2 3 8 6 4 2 0 2 4 6 8 x x y (regressor) D = { (x (n), y (n) ) } N, n=1 x (n+1) y (n+1), (
4d_06.dvi
Learning and Recognition of Time-Series Data Based on Self-Organizing Incremental Neural Network Shogo OKADA and Osamu HASEGAWA Self-Organizing Incremental Neural Network (SOINN) DP [12] DP SOINN HMM (Hidden
(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
kokyuroku.dvi
On Applications of Rigorous Computing to Dynamical Systems (Zin ARAI) Department of Mathematics, Kyoto University email: arai@math.kyoto-u.ac.jp 1 [12, 13] Lorenz 2 Lorenz 3 4 2 Lorenz 2.1 Lorenz E. Lorenz
2. Eades 1) Kamada-Kawai 7) Fruchterman 2) 6) ACE 8) HDE 9) Kruskal MDS 13) 11) Kruskal AGI Active Graph Interface 3) Kruskal 5) Kruskal 4) 3. Kruskal
1 2 3 A projection-based method for interactive 3D visualization of complex graphs Masanori Takami, 1 Hiroshi Hosobe 2 and Ken Wakita 3 Proposed is a new interaction technique to manipulate graph layouts
2008 : 80725872 1 2 2 3 2.1.......................................... 3 2.2....................................... 3 2.3......................................... 4 2.4 ()..................................
., White-Box, White-Box. White-Box.,, White-Box., Maple [11], 2. 1, QE, QE, 1 Redlog [7], QEPCAD [9], SyNRAC [8] 3 QE., 2 Brown White-Box. 3 White-Box
![., White-Box, White-Box. White-Box.,, White-Box., Maple [11], 2. 1, QE, QE, 1 Redlog [7], QEPCAD [9], SyNRAC [8] 3 QE., 2 Brown White-Box. 3 White-Box](/thumbs/91/105919953.jpg "., White-Box, White-Box. White-Box.,, White-Box., Maple [11], 2. 1, QE, QE, 1 Redlog [7], QEPCAD [9], SyNRAC [8] 3 QE., 2 Brown White-Box. 3 White-Box")
White-Box Takayuki Kunihiro Graduate School of Pure and Applied Sciences, University of Tsukuba Hidenao Iwane ( ) / Fujitsu Laboratories Ltd. / National Institute of Informatics. Yumi Wada Graduate School
A11 (1993,1994) 29 A12 (1994) 29 A13 Trefethen and Bau Numerical Linear Algebra (1997) 29 A14 (1999) 30 A15 (2003) 30 A16 (2004) 30 A17 (2007) 30 A18
2013 8 29y, 2016 10 29 1 2 2 Jordan 3 21 3 3 Jordan (1) 3 31 Jordan 4 32 Jordan 4 33 Jordan 6 34 Jordan 8 35 9 4 Jordan (2) 10 41 x 11 42 x 12 43 16 44 19 441 19 442 20 443 25 45 25 5 Jordan 26 A 26 A1
{x 1 -x 4, x 2 -x 5, x 3 -x 6 }={X, Y, Z} {X, Y, Z} EEC EIC Freeman (4) ANN Artificial Neural Network ANN Freeman mesoscopicscale 2.2 {X, Y, Z} X a (t
( ) No. 4-69 71 5 (5-5) *1 A Coupled Nonlinear Oscillator Model for Emergent Systems (2nd Report, Spatiotemporal Coupled Lorenz Model-based Subsystem) Tetsuji EMURA *2 *2 College of Human Sciences, Kinjo
report-MSPC.dvi
Multivariate Statistical Process Control 4 1 5 6 Copyright cfl4-5 by Manabu Kano. All rights reserved. 1 1 3 3.1 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :
x T = (x 1,, x M ) x T x M K C 1,, C K 22 x w y 1: 2 2
Takio Kurita Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology takio-kurita@aistgojp (Support Vector Machine, SVM) 1 (Support Vector Machine, SVM) ( ) 2
Real AdaBoost HOG 2009 3 A Graduation Thesis of College of Engineering, Chubu University Efficient Reducing Method of HOG Features for Human Detection based on Real AdaBoost Chika Matsushima ITS Graphics
Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate catego
Computational Semantics 1 category specificity Warrington (1975); Warrington & Shallice (1979, 1984) 2 basic level superiority 3 super-ordinate category preservation 1 / 13 analogy by vector space Figure
Microsoft Word - toyoshima-deim2011.doc
DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images
[1] SBS [2] SBS Random Forests[3] Random Forests ii
![[1] SBS [2] SBS Random Forests[3] Random Forests ii](/thumbs/74/70333192.jpg "[1] SBS [2] SBS Random Forests[3] Random Forests ii")
Random Forests 2013 3 A Graduation Thesis of College of Engineering, Chubu University Proposal of an efficient feature selection using the contribution rate of Random Forests Katsuya Shimazaki [1] SBS
03.Œk’ì
HRS KG NG-HRS NG-KG AIC Fama 1965 Mandelbrot Blattberg Gonedes t t Kariya, et. al. Nagahara ARCH EngleGARCH Bollerslev EGARCH Nelson GARCH Heynen, et. al. r n r n =σ n w n logσ n =α +βlogσ n 1 + v n w
スライド 1
61 SAS SAS LOHAS 18 18 12 01 LOHAS ( ) ( ) LOHAS 29% 35% LOHAS LOHAS 18 5 20 60 GMO 500 Yes No Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 LOHAS Q25 LOHAS / 2
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE {s-kasihr, wakamiya,
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 565-0871 1 5 E-mail: {s-kasihr, wakamiya, murata}@ist.osaka-u.ac.jp PC 70% Design, implementation, and evaluation
2 ( 3 2 ) DoE 2) ) (D ) y (x 1, x 2, xj,, x D ) 3 N Q i i l j X i, j 1 4 (x y ) 4 DoE 4 1σ ( 4 ) 3 4 ( ) ( )
HiL Thomas KRUSE Holger ULMER Tobias KREUZINGER Tobias LANG 2-3-5 C 17F ETAS K.K., Queen s Tower C-17F 2-3-5, Minatomirai, Nishi-ku, Yokohama, Kanagawa, Japan Robert Bosch GmbH, Diesel Gasoline Systems
4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
![4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q](/thumbs/92/110416683.jpg "4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q")
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
9 8 7 (x-1.0)*(x-1.0) *(x-1.0) (a) f(a) (b) f(a) Figure 1: f(a) a =1.0 (1) a 1.0 f(1.0)
E-mail: takio-kurita@aist.go.jp 1 ( ) CPU ( ) 2 1. a f(a) =(a 1.0) 2 (1) a ( ) 1(a) f(a) a (1) a f(a) a =2(a 1.0) (2) 2 0 a f(a) a =2(a 1.0) = 0 (3) 1 9 8 7 (x-1.0)*(x-1.0) 6 4 2.0*(x-1.0) 6 2 5 4 0 3-2
? (EM),, EM? (, 2004/ 2002) von Mises-Fisher ( 2004) HMM (MacKay 1997) LDA (Blei et al. 2001) PCFG ( 2004)... Variational Bayesian methods for Natural
SLC Internal tutorial Daichi Mochihashi daichi.mochihashi@atr.jp ATR SLC 2005.6.21 (Tue) 13:15 15:00@Meeting Room 1 Variational Bayesian methods for Natural Language Processing p.1/30 ? (EM),, EM? (, 2004/
ver.1 / c /(13)
1 -- 11 1 c 2010 1/(13) 1 -- 11 -- 1 1--1 1--1--1 2009 3 t R x R n 1 ẋ = f(t, x) f = ( f 1,, f n ) f x(t) = ϕ(x 0, t) x(0) = x 0 n f f t 1--1--2 2009 3 q = (q 1,..., q m ), p = (p 1,..., p m ) x = (q,
_314I01BM浅谷2.indd
587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4 588 31 4 2016
Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,
H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x
DEIM Forum 2019 A7-1 Flexible Distance-based Hashing mori
DEIM Forum 2019 A7-1 Flexible Distance-based Hashing 731 3194 E-mail: mc66023@e.hiroshima-cu.ac.jp,{wakaba,s naga,inagi,yoko}@hiroshima-cu.ac.jp, morikei18@gmail.com Flexible Distance-based Hashing(FDH)
lecture_rev3
Connectome Gigandet, Xavier, et al. "Estimating the confidence level of white matter connections obtained with MRI tractography." PLoS One 3.12 (2008): e4006. Hagmann, Patric, et al. "Mapping the structural
IPSJ SIG Technical Report GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1
1 1 1 GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1 and Hiroshi Ishiguro 1 Self-location is very informative for wearable systems.
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
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
鉄鋼協会プレゼン
NN :~:, 8 Nov., Adaptive H Control for Linear Slider with Friction Compensation positioning mechanism moving table stand manipulator Point to Point Control [G] Continuous Path Control ground Fig. Positoining
10:00 12:00 12:00 13:00 13:00 14:30 14:45 15:45 16:00 17: IRT 1 10:00 12:00 12:00 13:00 13:00 14:30 14:45 16: IRT 2 CAT
JART 2008 10:00 12:00 12:00 13:00 13:00 14:30 14:45 15:45 16:00 17:00 8 28 IRT 1 10:00 12:00 12:00 13:00 13:00 14:30 14:45 16:45 8 29 IRT 2 CAT PC Windows PC Microsoft Office 2007 Microsoft Office 2003
Dirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :
Dirichlet Process : joint work with: Max Welling (UC Irvine), Yee Whye Teh (UCL, Gatsby) http://kenichi.kurihara.googlepages.com/miru_workshop.pdf 1 /40 MIRU2008 : Dirichlet process mixture Dirichlet process
Vol.-CVIM-7 No.7 /3/8 NLPCA kernel PCA KPCA 4),) NLPCA KPCA NLPCA KPCA principle curve principle surface KPCA ) ),),6),8),),3) ) Jacobian KPCA PCA ) P
Vol.-CVIM-7 No.7 /3/8 RANSAC M Subspace fitting via robust Jacobian kernel PCA Jun Fujiki and Shotaro Akaho The subspace fitting method based on the original kernel principle component analysis (PCA),
01.Œk’ì/“²fi¡*
AIC AIC y n r n = logy n = logy n logy n ARCHEngle r n = σ n w n logσ n 2 = α + β w n 2 () r n = σ n w n logσ n 2 = α + β logσ n 2 + v n (2) w n r n logr n 2 = logσ n 2 + logw n 2 logσ n 2 = α +β logσ
fiš„v2.dvi
(2001) 49 1 9 21 * 2000 12 27 2001 3 19 (PCA) (MDS) MDS Young Yamane AIT MDS MDS Makioka 2 MDS MDS PCA, MDS. 1. 140 Yes * 351 0198 2 1 Figures 1 and 3: Reprinted with permission from Young, P. M. and Yamane,
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
1) (AI) 5G AI AI Google IT Deep Learning RWC (RWCP) RWC Web RWCP [1] 2. RWC ETL-Mark I, II (1952, 1955) (ETL) (ETL-Mark
![1) (AI) 5G AI AI Google IT Deep Learning RWC (RWCP) RWC Web RWCP [1] 2. RWC ETL-Mark I, II (1952, 1955) (ETL) (ETL-Mark](/thumbs/72/67554763.jpg "1) (AI) 5G AI AI Google IT Deep Learning RWC (RWCP) RWC Web RWCP [1] 2. RWC ETL-Mark I, II (1952, 1955) (ETL) (ETL-Mark")
RWC 10 RWC 1992 4 2001 13 RWC 21 RWC 1. RWC (Real World Computing ) (1982 1992 ) 3 1992 2001 10 21 RWCP 5 1992 1996 5 1997 2001 RWC (RWI) (PDC) 2 RWC (5G) 1 1) (AI) 5G AI AI Google IT Deep Learning RWC
[18] [22] YouTube 2 Wittner taktell piccolino amazon.co.jp
![[18] [22] YouTube 2 Wittner taktell piccolino amazon.co.jp](/thumbs/91/104676623.jpg "[18] [22] YouTube 2 Wittner taktell piccolino amazon.co.jp")
メトロノームの同期現象 宮崎大学 工学部 情報システム工学科 1 伊達 章 はじめに 1 日のリズム 1 週間のリズム 1 年のリズム 世の中にはいろいろなリズムがある 天 体の運動 呼吸 時計 もっと速いのはコンピュータのクロックシグナル 我々はいろいろ なリズムに囲まれて生活している ここでは単純に ほぼ一定の周期で同じ出来事が繰り返 す現象を リズム現象と呼ぼう 一つのリズムだけを考えると同じ出来事が繰り返すだけで
http://www.ieice-hbkb.org/ 2 2 1 2 -- 2 -- 1 1--1 2011 2 26 26 1 1 AD 1 1 1-2 1-3 c 2013 2/(19)
2 -- 2 1 2011 2 1-1 1-2 SVM Boosting 1-3 1-4 c 2013 1/(19) http://www.ieice-hbkb.org/ 2 2 1 2 -- 2 -- 1 1--1 2011 2 26 26 1 1 AD 1 1 1-2 1-3 c 2013 2/(19) 1-4 c 2013 3/(19) 2 -- 2 -- 1 1--2 2011 2 Principal
: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99
218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.
22_05.dvi
Vol. 1 No. 2 41 49 (July 2008) 3 1 1 3 2 1 1 3 Person-independent Monocular Tracking of Face and Facial Actions Yusuke Sugano 1 and Yoichi Sato 1 This paper presents a monocular method of tracking faces
Trial for Value Quantification from Exceptional Utterances 37-066593 1 5 1.1.................................. 5 1.2................................ 8 2 9 2.1.............................. 9 2.1.1.........................
untitled
- - GRIPS 1 traceroute IP Autonomous System Level http://opte.org/ GRIPS 2 Network Science http://opte.org http://research.lumeta.com/ches/map http://www.caida.org/home http://www.imdb.com http://citeseer.ist.psu.edu
untitled
c 645 2 1. GM 1959 Lindsey [1] 1960 Howard [2] Howard 1 25 (Markov Decision Process) 3 3 2 3 +1=25 9 Bellman [3] 1 Bellman 1 k 980 8576 27 1 015 0055 84 4 1977 D Esopo and Lefkowitz [4] 1 (SI) Cover and
07.報文_及川ら-二校目.indd
8 01 01 4 4 1 5 16 18 6 006 H 18 4 011 H 6 4 1 5 1 5 007 H 19 5 009 1 5 006 007 009 011 9 10 4 000 H 1 4 5 004 H 16 4 004 009 H 1 5 4 4 5 1 4 006 011 1 1 4m 5m 10m 007 1 7 009 009 1 5 10 1 000kg 10a 006
2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i
1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,
untitled
...1...3 -... 3 -... 3...5 -... 5 -... 5 -... 6 -... 7 -... 7 -... 7 -... 10 -... 17 -... 17 -... 18 -... 20 -... 21 1 TEL 2 3 4 ) ) 5 6 7 8 9 10 11 () (4) () (4) () () 730-0042730-8586 730-0042 730-0042730-8586
..,,,, , ( ) 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
main.dvi
DEIM Forum 2018 J7-3 305-8573 1-1-1 305-8573 1-1-1 305-8573 1-1-1 () 151-0053 1-3-15 6F URL SVM Identifying Know-How Sites basedonatopicmodelandclassifierlearning Jiaqi LI,ChenZHAO, Youchao LIN, Ding YI,ShutoKAWABATA,
2 2. d T = (V, E) d i D d i.time d i D (d i) = {d D ; d.time < d i.time} d i d D (d i) ρ(d, d i) ˆd d i V V {d i}, P (d i) = ˆd = arg max d D (d i ) ρ
DEIM Forum 207 G7-3 305 8550 2 E-mail: {fushimi,satoh}@ce.slis.tsukuba.ac.jp Web. Web Minimum Spanning TreeRelative Neighborhood Graphk-NN Graph 2 3 [] Principal Component AnalysisMulti-Dimensional Scaling
(note-02) Rademacher 1/57
(note-02) Rademacher 1/57 (x 1, y 1 ),..., (x n, y n ) X Y f : X Y Y = R f Y = {+1, 1}, {1, 2,..., G} f x y 1. (x 1, y 1 ),..., (x n, y n ) f(x i ) ( ) 2. x f(x) Y 2/57 (x, y) f(x) f(x) y (, loss) l(f(x),
2005 1
2005 1 1 1 2 2 2.1....................................... 2 2.2................................... 5 2.3 VSWR................................. 6 2.4 VSWR 2............................ 7 2.5.......................................
通信容量制約を考慮したフィードバック制御 - 電子情報通信学会 情報理論研究会（IT） 若手研究者のための講演会
IT 1 2 1 2 27 11 24 15:20 16:05 ( ) 27 11 24 1 / 49 1 1940 Witsenhausen 2 3 ( ) 27 11 24 2 / 49 1940 2 gun director Warren Weaver, NDRC (National Defence Research Committee) Final report D-2 project #2,
(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]
![(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]](/thumbs/49/25500502.jpg "(b) BoF codeword codeword BoF (c) BoF Fergus Weber [11] Weber [12] Weber Fergus BoF (b) Fergus [13] Fergus 2. Fergus 2. 1 Fergus [3]")
* A Multimodal Constellation Model for Generic Object Recognition Yasunori KAMIYA, Tomokazu TAKAHASHI,IchiroIDE, and Hiroshi MURASE Bag of Features (BoF) BoF EM 1. [1] Part-based Graduate School of Information
untitled
IT E- IT http://www.ipa.go.jp/security/ CERT/CC http://www.cert.org/stats/#alerts IPA IPA 2004 52,151 IT 2003 12 Yahoo 451 40 2002 4 18 IT 1/14 2.1 DoS(Denial of Access) IDS(Intrusion Detection System)
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
1 2,a) 3 1,b) 2017 1 17, 2017 6 6 A EM A Latent Class Model to Analyze the Relationship Between Companies Appeal Points and Students Reasons for Application Teppei Sakamoto 1 Haruka Yamashita 2,a) Tairiku
音響モデル triphone 入力音声 音声分析 デコーダ 言語モデル N-gram bigram HMM の状態確率として利用 出力層 triphone: 3003 ノード リスコア trigram 隠れ層 2048 ノード X7 層 1 Structure of recognition syst
1,a) 1 1 1 deep neural netowrk(dnn) (HMM) () GMM-HMM 2 3 (CSJ) 1. DNN [6]. GPGPU HMM DNN HMM () [7]. [8] [1][2][3] GMM-HMM Gaussian mixture HMM(GMM- HMM) MAP MLLR [4] [3] DNN 1 1 triphone bigram [5]. 2
IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,
![IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,](/thumbs/94/119099134.jpg "IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,")
1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] 1 599 8531 1 1 Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai, Osaka 599 8531, Japan 2 565 0871 Osaka University 1 1, Yamadaoka, Suita, Osaka
( 9 1 ) 1 2 1.1................................... 2 1.2................................................. 3 1.3............................................... 4 1.4...........................................
<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
MATLAB/Simulink による現代制御入門 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. http://www.morikita.co.jp/books/mid/9241 このサンプルページの内容は, 初版 1 刷発行当時のものです. i MATLAB/Simulink MATLAB/Simulink 1. 1 2. 3. MATLAB/Simulink
NP 1 ( ) Ehrgott [3] ( ) (Ehrgott [3] ) Ulungu & Teghem [8] Zitzler, Laumanns & Bleuler [11] Papadimitriou & Yannakakis [7] Zaroliagis [10] 2 1
![NP 1 ( ) Ehrgott [3] ( ) (Ehrgott [3] ) Ulungu & Teghem [8] Zitzler, Laumanns & Bleuler [11] Papadimitriou & Yannakakis [7] Zaroliagis [10] 2 1](/thumbs/94/118607815.jpg "NP 1 ( ) Ehrgott [3] ( ) (Ehrgott [3] ) Ulungu & Teghem [8] Zitzler, Laumanns & Bleuler [11] Papadimitriou & Yannakakis [7] Zaroliagis [10] 2 1")
NP 1 ( ) Ehrgott [3] 2 1 1 ( ) (Ehrgott [3] ) Ulungu & Teghem [8] Zitzler, Laumanns & Bleuler [11] Papadimitriou & Yannakakis [7] Zaroliagis [10] 2 1 1 1 Avis & Fukuda [1] 2 NP (Ehrgott [3] ) ( ) 3 NP
Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate
25 22 1 2 2 4 2.1........................ 5 2.2.................. 7 3 9 3.1................................. 9 3.2............................. 10 3.3................. 10 3.4....................... 12
ばらつき抑制のための確率最適制御
( ) http://wwwhayanuemnagoya-uacjp/ fujimoto/ 2011 3 9 11 ( ) 2011/03/09-11 1 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 2 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 3 / 46 (1/2) r + Controller - u Plant y
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
![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](/thumbs/89/99361107.jpg "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")
ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
130 Oct Radial Basis Function RBF Efficient Market Hypothesis Fama ) 4) 1 Fig. 1 Utility function. 2 Fig. 2 Value function. (1) (2)
Vol. 47 No. SIG 14(TOM 15) Oct. 2006 RBF 2 Effect of Stock Investor Agent According to Framing Effect to Stock Exchange in Artificial Stock Market Zhai Fei, Shen Kan, Yusuke Namikawa and Eisuke Kita Several
Optical Flow t t + δt 1 Motion Field 3 3 1) 2) 3) Lucas-Kanade 4) 1 t (x, y) I(x, y, t)
http://wwwieice-hbkborg/ 2 2 4 2 -- 2 4 2010 9 3 3 4-1 Lucas-Kanade 4-2 Mean Shift 3 4-3 2 c 2013 1/(18) http://wwwieice-hbkborg/ 2 2 4 2 -- 2 -- 4 4--1 2010 9 4--1--1 Optical Flow t t + δt 1 Motion Field
ẋ = ax + y ẏ = x x by 2 Griffith a b Saddle Node Saddle-Node (phase plane) Griffith mrna(y) Protein(x) (nullcline) 0 (nullcline) (
2 (bifurcation) Saddle-Node Hopf Pitchfork 2.1 Saddle-Node( ) 2.1.1 Griffith : (bistability) ON/OFF 2 (bistability) (Stable node Stable spiral) 2 Griffith X mrna mrna X Griffith ( x y mrna ) 2.1: Griffith
1 4 1 ( ) ( ) ( ) ( ) () 1 4 2
7 1995, 2017 7 21 1 2 2 3 3 4 4 6 (1).................................... 6 (2)..................................... 6 (3) t................. 9 5 11 (1)......................................... 11 (2)
CVaR
CVaR 20 4 24 3 24 1 31 ,.,.,. Markowitz,., (Value-at-Risk, VaR) (Conditional Value-at-Risk, CVaR). VaR, CVaR VaR. CVaR, CVaR. CVaR,,.,.,,,.,,. 1 5 2 VaR CVaR 6 2.1................................................
untitled
CFD JAXA CFD CFD Navier-Stokes -1- -2-1 CFD CFD Navier-Stokes 1. 2. 2,500,000 CFD Copyright Boeing CFD Boeing B777 HP -3- -4-2 CFD European Transonic Wind tunnel (-163 ) JAXA 2mx2m CFD (Computational Fluid
V T n n = A r n A n r n U V m m n n UT U = I V T V = I : A = A = UΣV T A T AV = VΣ T Σ : AB T = B T A T V A T A V A V T V = I 3 V A V T V = I : A AK =
PLS Janes PLS PLS PCR MLR PCA singular value decomposition : m n A 3 A = U m n m m Σ m n VT n n U left singular matrix V Σ U = m m A m r Σ = m n σ σ r A m m r V T n n = A r n A n r n U V m m n n UT U =
Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4]
![Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4]](/thumbs/91/107302309.jpg "Fig. 2 28th Ryuou Tournament, Match 5, 59th move. The last move is Black s Rx5f. 1 Tic-Tac-Toe Fig. 1 AsearchtreeofTic-Tac-Toe. [2] [3], [4]")
1,a) 2 3 2017 4 6, 2017 9 5 Predicting Moves in Comments for Shogi Commentary Generation Hirotaka Kameko 1,a) Shinsuke Mori 2 Yoshimasa Tsuruoka 3 Received: April 6, 2017, Accepted: September 5, 2017 Abstract:
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)
![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)](/thumbs/68/59204737.jpg "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)")
(MIRU2012) 2012 8 820-8502 680-4 E-mail: {d kouno,shimada,endo}@pluto.ai.kyutech.ac.jp (1) (2) (3) (4) 4 AdaBoost 1. Kanade [6] CLAFIC [12] EigenFace [10] 1 1 2 1 [7] 3 2 2 (1) (2) (3) (4) 4 4 AdaBoost
7 OpenFOAM 6) OpenFOAM (Fujitsu PRIMERGY BX9, TFLOPS) Fluent 8) ( ) 9, 1) 11 13) OpenFOAM - realizable k-ε 1) Launder-Gibson 15) OpenFOAM 1.6 CFD ( )
71 特集 オープンソースの大きな流れ Nonlinear Sloshing Analysis in a Three-dimensional Rectangular Pool Ken UZAWA, The Center for Computational Sciences and E-systems, Japan Atomic Energy Agency 1 1.1 ( ) (RIST) (ORNL/RSICC)