Dirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :
|
|
- ただきよ すみだ
- 7 years ago
- Views:
Transcription
1 Dirichlet Process : joint work with: Max Welling (UC Irvine), Yee Whye Teh (UCL, Gatsby) 1 /40 MIRU2008 :
2 Dirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :
3 ? 3 /40 MIRU2008 :
4 Non-parametric Bayesian Model for Spectral Clustering etric Bayes, spectral clustering, probabilistic model Abstract dy the problem of searching for the r of clusters, k, in clustering. In clustering applications, spectral clushas achieved great success. Follows success, we consider an extension of l clustering based on a non-parametric an approach which gives an elegant sofor model selection, i.e. choosing k. In linkage analysis lar, we use the Dirichlet process (DP). t propose a generative model for specstering to apply the DP. We then show relaxed greedy maximum likelihood esn for the model is in fact equivalent to l clustering. Based on the generative we derive a non-parametric Bayesian for spectral clustering using the DP. mental results show that the proposed k-means spectral clustering 4 /40 MIRU2008 : Figure 3: Typical results for BKM for various values of τ = 0.1, 0.5, 2. The tru DP Gaussian mixture Proposed algorithm to ten. In these 30 plots, BKM found eight, nine and 30ten clusters respectively. Som too small or too 15 large to be visible Dirichlet Process Mixture Figure Typical clustering results by the Dirichlet process Gaussian 6000 mixture model (left) and the proposed algorithm (right) The former discovered 10 Gaussians, and the latter correctly discovered 3 clusters clustering algorithms even when the distribution of 1000 data can not be captured by usual distributions, e.g. Gaussian SSSSSSSSSSSSSSSSNSSSSSNSNNNNNNNNNNNSNNNSNSNNNNNNNN or multinomial. Largely speaking, there are
5 ( ) ( ) Dirichlet process EM-like MCMC 5 /40 MIRU2008 :
6 Dirichlet process mixture Dirichlet process mixture 6 /40 MIRU2008 :
7 7 /40 MIRU2008 :
8 7 /40 MIRU2008 :
9 ? z θ xi X: zi = 1 xk θ1 2 zk = 2 xj xl zj = 1 1 θ2 zl = 2 8 /40 MIRU2008 :
10 (Z, θ) = argmax log p(x Z,θ) xi X: zi = 1 xk θ1 2 zk = 2 xj xl zj = 1 1 θ2 zl = 2 9 /40 MIRU2008 :
11 (MCMC) 10/40 MIRU2008 :
12 Z=argmax p(x,z,θ) θ=argmax p(x,z,θ) q(z) p(x,z,θ) θ=argmax Eq(Z)[ log p(x,z,θ)] q(z) exp Eq(θ)[ log p(x,z,θ)] q(θ) exp Eq(Z)[ log p(x,z,θ)] 11/40 MIRU2008 :
13 Markov Chain Monte Carlo p(z X) Metropolis-Hastings Gibbs sampler ( ) %&! %&"' %&" %&#' %&# %&$' %&$ %&%' %!!!"!#!$ % $ # "! 12/40 MIRU2008 :
14 Dirichlet process mixture Dirichlet process mixture 13/40 MIRU2008 :
15 (Z, θ) = argmax log p(x Z,θ) iterated conditional mode Z=argmax log p(x Z,θ) θ=argmax log p(x Z,θ) θ1 zi = 1 zk = 2 zj = 1 1 θ2 zl = 2 14/40 MIRU2008 :
16 log p(x Z, θ) = 1 2 n x i θ zi 2 + constant i=1 z i = argmax log p(x, Z, θ) = argmax 1 2 x i θ zi 2 θ j = argmax log p(x, Z, θ) = 1 n j i;z i =j x i θ1 zj = 1 1 zi = 1 θ2 zk = 2 zl = 2 15/40 MIRU2008 :
17 : (Z, θ) = argmax p(x Z,θ) iterated conditoinal mode k-means z i = argmax x i θ zi 2 θ j = 1 x i n j i;z i =j θ1 zj = 1 1 zi = 1 θ2 zk = 2 zl = 2 16/40 MIRU2008 :
18 : 3 : 17/40 MIRU2008 :
19 K? K K Dirichlet process mixture K 20 0!20!40!60!60!40! !20!40!60!60!40! !20!40 K=3 K=4 K=5!60!60!40! /40 MIRU2008 :
20 Dirichlet process mixture Dirichlet process mixture 19/40 MIRU2008 :
21 Dirichlet Process Mixture K Dirichlet Process Mixture (DPM) K [Ferguson 73, Antoniak 74] K? 20/40 MIRU2008 :
22 21 K? ( ) e.g. (DPM) /40 MIRU2008 :
23 DPM p(z,k) (=p(z)) 3 x1, x2, x3 : p(x Z) ( :) e.g. (DPM) 22/40 MIRU2008 :
24 DPM 23/40 MIRU2008 :
25 DPM 23/40 MIRU2008 :
26 DPM 23/40 MIRU2008 :
27 DPM 23/40 MIRU2008 :
28 Chinese Restaurant Process Dirichlet process p(z N [z 1...z N 1 ]) = N N c α+n 1 α α+n 1 (Nc N > 0; Z N is an existing cluster.) (Nc N = 0; Z N is a new cluster.) 24/40 MIRU2008 :
29 DPM? e.g. (DPM) Dirichlet process mixture (DPM) Dirichlet p(z K) + Poisson p(k) etc. DPM (MCMC) Dirichlet + Poisson 25/40 MIRU2008 :
30 DPM consistency x1, x2, x3 consistency p(z2) + p(z5) = p([(x1,x2)]) p(z2)=α/(1+α)(2+α); p(z5) = 2/(1+α)(2+α) p([(x1,x2)]) = 1/(1+α) 26/40 MIRU2008 :
31 K DPM K DPM p(z,k) DPM DPM consistency 27/40 MIRU2008 :
32 note: K K 28/40 MIRU2008 :
33 Dirichlet process mixture Dirichlet process mixture 29/40 MIRU2008 :
34 iterated conditional mode EM MCMC Gibbs sampler 30/40 MIRU2008 :
35 iterated conditional mode EM MCMC Gibbs sampler 31/40 MIRU2008 :
36 Markov Chain Monte Carlo p(z X) Metropolis-Hastings Gibbs sampler ( ) %&! %&"' %&" %&#' %&# %&$' %&$ %&%' %!!!"!#!$ % $ # "! 32/40 MIRU2008 :
37 Gibbs Sampler ( ) Z = (z1, z2,..., zn) zi p(zi Z-i, X) p(z X) p(zi Z-i, X) Metroplis-Hastings zi p(zi Z-i, X) p(x,z) (DPM OK ) 33/40 MIRU2008 :
38 : θ={µ,σ}: µ Σ µ Σ Wishart θ 34/40 MIRU2008 :
39 DPM Gibbs Sampler 1. p(x Z,θ) e.g. 2. p(θ) p(z) DPM 3. p(x,z) = dθ p(x,z,θ) ( ) 4. zi p(zi Z-i, X) p(x,z) 35/40 MIRU2008 :
40 MNIST 88 Gibbs sampler (GS) 36/40 MIRU2008 :
41 !" #" $%" )!" #" $%" )!('#!&!('#!&!&'% Latent Dirichlet!&'! Allocation!&'&!&'#!* Gibbs sampler +,-./,!&'! "!""" #""" $%"""!&'%!&'%!&'!!&'!!&'(!&'#!&'(!#!&'# "!""" #""" $%""" *+,-.+!# "!""" #""" $%"""!&'! *+,-.+ GHDP 100 GHDP 1 GLDA CVHDP CVLDA VLDA!&'&!&'( 37/40 MIRU2008 :
42 DPM? K (DPM ) DPM p(z,k) DPM DPM consistent Gibbs sampler non-dpm () 38/40 MIRU2008 :
43 DPM hierarchical Dirichlet process HDP-LDA, HDP-HMM, HDP-PCFG 39/40 MIRU2008 :
44 Dirichlet process mixture Dirichlet process mixture 40/40 MIRU2008 :
IPSJ SIG Technical Report Pitman-Yor 1 1 Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Aki
Pitman-Yor Pitman-Yor n-gram A proposal of the melody generation method using hierarchical pitman-yor language model Akira Shirai and Tadahiro Taniguchi Although a lot of melody generation method has been
More informationJOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alterna
JOURNAL OF THE JAPANESE ASSOCIATION FOR PETROLEUM TECHNOLOGY VOL. 66, NO. 6 (Nov., 2001) (Received August 10, 2001; accepted November 9, 2001) Alternative approach using the Monte Carlo simulation to evaluate
More information12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
More 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
More information/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
More informationX 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
More informationClustering in Time and Periodicity of Strong Earthquakes in Tokyo Masami OKADA Kobe Marine Observatory (Received on March 30, 1977) The clustering in time and periodicity of earthquake occurrence are investigated
More information28 Horizontal angle correction using straight line detection in an equirectangular image
28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image
More information1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
More informationIsogai, 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
More informationChapter16
16 Flat Clustering (cluster) 16.1 (unsupervised learning) 16.1 3 17 (13 237 ) (distance measure) 16.1 2 3 (flat clustering) (hierarchical clustering) 17 17 2 (hard) (soft) (Latent semantic indexing) (18
More information? (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/
More informationTHE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly
More information4. 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
More informationThe Empirical Study on New Product Concept of the Dish Washer Abstract
The Empirical Study on New Product Concept of the Dish Washer Abstract t t Cluster Analysis For Applications International Conference on Quality 96 in Yokohama Clustering Algorithms
More informationkubostat7f p GLM! logistic regression as usual? N? GLM GLM doesn t work! GLM!! probabilit distribution binomial distribution : : β + β x i link functi
kubostat7f p statistaical models appeared in the class 7 (f) kubo@eeshokudaiacjp https://googl/z9cjy 7 : 7 : The development of linear models Hierarchical Baesian Model Be more flexible Generalized Linear
More information03.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
More informationIT,, 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
More informationMPC 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
MPC Inventory Manegement via Model Predictive Control 1 1 1,2,3 Yoshinobu Matsui 1 Yuhei Umeda 1 Hirokazu Anai 1,2,3 1 1 FUJITSULABORATORIES LTD. 2 2 Kyushu University IMI 3 3 National Institute of Informatics
More information(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
More informationuntitled
2009 57 2 393 411 c 2009 1 1 1 2009 1 15 7 21 7 22 1 1 1 1 1 1 1 1. 1 1 1 2 3 4 12 2000 147 31 1 3,941 596 1 528 1 372 1 1 1.42 350 1197 1 13 1 394 57 2 2009 1 1 19 2002 2005 4.8 1968 5 93SNA 6 12 1 7,
More information橡ボーダーライン.PDF
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 To be or not to be 32 33 34 35 36 37 38 ( ) 39 40 41 42 43 44 45 46 47 48 ( ) 49 50 51 52
More information揃 24 1681 0 20 40 60 80 100 0 21 42 63 84 Lag [hour] Lag [day] 35
Forecasting Model for Electricity Consumption in Residential House Based on Time Series Analysis * ** *** Shuhei Kondo Nobayasi Masamori Shuichi Hokoi ( 2015 7 3 2015 12 11 ) After the experience of electric
More informationmain.dvi
1 1 1 2 3 LDA Estimating and Analyzing a Domain Topic Model of Entries Kensaku Makita 1 Hiroko Suzuki 1 Daichi Koike 1 Takehito Utsuro 2 Yasuhide Kawada 3 Abstract: In order to address the issue of quickly
More informationBy Kenji Kinoshita, I taru Fukuda, Taiji Ota A Study on the Use of Overseas Construction Materials There are not few things which are superior in the price and the aspect of the quality to a domestic
More information24 Depth scaling of binocular stereopsis by observer s own movements
24 Depth scaling of binocular stereopsis by observer s own movements 1130313 2013 3 1 3D 3D 3D 2 2 i Abstract Depth scaling of binocular stereopsis by observer s own movements It will become more usual
More information23 The Study of support narrowing down goods on electronic commerce sites
23 The Study of support narrowing down goods on electronic commerce sites 1120256 2012 3 15 i Abstract The Study of support narrowing down goods on electronic commerce sites Masaki HASHIMURA Recently,
More information60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77
60 (W30)? 1. ( ) kubo@ees.hokudai.ac.jp 2. ( ) web site URL http://goo.gl/e1cja!! 2013 03 07 (2013 03 07 17 :41 ) 1/ 77 ! : :? 2013 03 07 (2013 03 07 17 :41 ) 2/ 77 2013 03 07 (2013 03 07 17 :41 ) 3/ 77!!
More informationThe Indirect Support to Faculty Advisers of die Individual Learning Support System for Underachieving Student The Indirect Support to Faculty Advisers of the Individual Learning Support System for Underachieving
More information浜松医科大学紀要
On the Statistical Bias Found in the Horse Racing Data (1) Akio NODA Mathematics Abstract: The purpose of the present paper is to report what type of statistical bias the author has found in the horse
More information80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = i=1 i=1 n λ x i e λ i=1 x i! = λ n i=1 x i e nλ n i=1 x
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
More information24 Region-Based Image Retrieval using Fuzzy Clustering
24 Region-Based Image Retrieval using Fuzzy Clustering 1130323 2013 3 9 Visual-key Image Retrieval(VKIR) k-means Fuzzy C-means 2 200 2 2 20 VKIR 5 18% 54% 7 30 Fuzzy C-means i Abstract Region-Based Image
More informationn-jas09.dvi
Vol. 9 (2009 12 ), No. 03-091211 JASCOME CREEP ANALYSIS DISCONTINUOUS ROCK MASS AROUND UNDERGROUND CAVERN 1) 2) 3) Takakuni TATSUMI, Hidenori YOSHIDA and Masumi FUJIWARA 1) ( 761-0396 2217-20, E-mail:
More information16_.....E...._.I.v2006
55 1 18 Bull. Nara Univ. Educ., Vol. 55, No.1 (Cult. & Soc.), 2006 165 2002 * 18 Collaboration Between a School Athletic Club and a Community Sports Club A Case Study of SOLESTRELLA NARA 2002 Rie TAKAMURA
More informationIPSJ SIG Technical Report Vol.2012-MUS-96 No /8/10 MIDI Modeling Performance Indeterminacies for Polyphonic Midi Score Following and
MIDI 1 2 3 2 1 Modeling Performance Indeterminacies for Polyphonic Midi Score Following and Its Application to Automatic Accompaniment Nakamura Eita 1 Yamamoto Ryuichi 2 Saito Yasuyuki 3 Sako Shinji 2
More information1 Tokyo Daily Rainfall (mm) Days (mm)
( ) r-taka@maritime.kobe-u.ac.jp 1 Tokyo Daily Rainfall (mm) 0 100 200 300 0 10000 20000 30000 40000 50000 Days (mm) 1876 1 1 2013 12 31 Tokyo, 1876 Daily Rainfall (mm) 0 50 100 150 0 100 200 300 Tokyo,
More information10:30 12:00 P.G. vs vs vs 2
1 10:30 12:00 P.G. vs vs vs 2 LOGIT PROBIT TOBIT mean median mode CV 3 4 5 0.5 1000 6 45 7 P(A B) = P(A) + P(B) - P(A B) P(B A)=P(A B)/P(A) P(A B)=P(B A) P(A) P(A B) P(A) P(B A) P(B) P(A B) P(A) P(B) P(B
More information( ) [1] [4] ( ) 2. [5] [6] Piano Tutor[7] [1], [2], [8], [9] Radiobaton[10] Two Finger Piano[11] Coloring-in Piano[12] ism[13] MIDI MIDI 1 Fig. 1 Syst
情報処理学会インタラクション 2015 IPSJ Interaction 2015 15INT014 2015/3/7 1,a) 1,b) 1,c) Design and Implementation of a Piano Learning Support System Considering Motivation Fukuya Yuto 1,a) Takegawa Yoshinari 1,b) Yanagi
More information橡最終原稿.PDF
GIS Simulation analysis of disseminate of disaster information using GIS * ** *** Toshitaka KATADAJunsaku ASADA and Noriyuki KUWASAWA GIS GIS AbstractWe have developed the simulation model expressing the
More informationï\éÜA4*
Feature Article Imaging of minuscule amounts of chemicals, Scannimg Chemical Microscope --- Increasing analysis information through imaging --- Abstract We have developed a Scanning Chemical Microscope
More informationMicrosoft 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
More information1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [
Vol.2, No.x, April 2015, pp.xx-xx ISSN xxxx-xxxx 2015 4 30 2015 5 25 253-8550 1100 Tel 0467-53-2111( ) Fax 0467-54-3734 http://www.bunkyo.ac.jp/faculty/business/ 1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The
More information分布
(normal distribution) 30 2 Skewed graph 1 2 (variance) s 2 = 1/(n-1) (xi x) 2 x = mean, s = variance (variance) (standard deviation) SD = SQR (var) or 8 8 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 8 0 1 8 (probability
More information1,a) 1,b) TUBSTAP TUBSTAP Offering New Benchmark Maps for Turn Based Strategy Game Tomihiro Kimura 1,a) Kokolo Ikeda 1,b) Abstract: Tsume-shogi and Ts
JAIST Reposi https://dspace.j Title ターン制戦略ゲームにおけるベンチマークマップの提 案 Author(s) 木村, 富宏 ; 池田, 心 Citation ゲームプログラミングワークショップ 2016 論文集, 2016: 36-43 Issue Date 2016-10-28 Type Conference Paper Text version author
More information189 2015 1 80
189 2015 1 A Design and Implementation of the Digital Annotation Basis on an Image Resource for a Touch Operation TSUDA Mitsuhiro 79 189 2015 1 80 81 189 2015 1 82 83 189 2015 1 84 85 189 2015 1 86 87
More informationkut-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
More information〈論文〉興行データベースから「古典芸能」の定義を考える
Abstract The long performance database of rakugo and kabuki was totaled, and it is found that few programs are repeated in both genres both have the frequency differential of performance. It is a question
More information258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
More informationIPSJ SIG Technical Report Vol.2016-CE-137 No /12/ e β /α α β β / α A judgment method of difficulty of task for a learner using simple
1 2 3 4 5 e β /α α β β / α A judgment method of difficulty of task for a learner using simple electroencephalograph Katsuyuki Umezawa 1 Takashi Ishida 2 Tomohiko Saito 3 Makoto Nakazawa 4 Shigeichi Hirasawa
More informationkubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib
kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit
More informationVol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka
Vol. 43 No. 2 Feb. 2002,, MIDI A Probabilistic-model-based Quantization Method for Estimating the Position of Onset Time in a Score Masatoshi Hamanaka, Masataka Goto,, Hideki Asoh and Nobuyuki Otsu, This
More information21 Key Exchange method for portable terminal with direct input by user
21 Key Exchange method for portable terminal with direct input by user 1110251 2011 3 17 Diffie-Hellman,..,,,,.,, 2.,.,..,,.,, Diffie-Hellman, i Abstract Key Exchange method for portable terminal with
More information,,.,.,,.,.,.,.,,.,..,,,, 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
More informationOverview (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), (
More information4 i
22 Quantum error correction and its simulation 1135071 2011 3 1 4 i Abstract Quantum error correction and its simulation Hiroko Dehare Researches in quantum information theory and technology, that mix
More information,,,,., 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
More information* n x 11,, x 1n N(µ 1, σ 2 ) x 21,, x 2n N(µ 2, σ 2 ) H 0 µ 1 = µ 2 (= µ ) H 1 µ 1 µ 2 H 0, H 1 *2 σ 2 σ 2 0, σ 2 1 *1 *2 H 0 H
1 1 1.1 *1 1. 1.3.1 n x 11,, x 1n Nµ 1, σ x 1,, x n Nµ, σ H 0 µ 1 = µ = µ H 1 µ 1 µ H 0, H 1 * σ σ 0, σ 1 *1 * H 0 H 0, H 1 H 1 1 H 0 µ, σ 0 H 1 µ 1, µ, σ 1 L 0 µ, σ x L 1 µ 1, µ, σ x x H 0 L 0 µ, σ 0
More information22 1,936, ,115, , , , , , ,
21 * 2 3 1 1991 1945 200 60 1944 No. 41 2016 22 1,936,843 1945 1,115,594 1946 647,006 1947 598,507 1 60 2014 501,230 354,503 5 2009 405,571 5 1 2 2009 2014 5 37,285 1 2 1965 10 1975 66 1985 43 10 3 1990
More informationII 2 II
II 2 II 2005 yugami@cc.utsunomiya-u.ac.jp 2005 4 1 1 2 5 2.1.................................... 5 2.2................................. 6 2.3............................. 6 2.4.................................
More information_念3)医療2009_夏.indd
Evaluation of the Social Benefits of the Regional Medical System Based on Land Price Information -A Hedonic Valuation of the Sense of Relief Provided by Health Care Facilities- Takuma Sugahara Ph.D. Abstract
More informationEQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Ju
EQUIVALENT TRANSFORMATION TECHNIQUE FOR ISLANDING DETECTION METHODS OF SYNCHRONOUS GENERATOR -REACTIVE POWER PERTURBATION METHODS USING AVR OR SVC- Jun Motohashi, Member, Takashi Ichinose, Member (Tokyo
More informationsoturon.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
More informationkubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or
kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4
More information2 G(k) e ikx = (ik) n x n n! n=0 (k ) ( ) X n = ( i) n n k n G(k) k=0 F (k) ln G(k) = ln e ikx n κ n F (k) = F (k) (ik) n n= n! κ n κ n = ( i) n n k n
. X {x, x 2, x 3,... x n } X X {, 2, 3, 4, 5, 6} X x i P i. 0 P i 2. n P i = 3. P (i ω) = i ω P i P 3 {x, x 2, x 3,... x n } ω P i = 6 X f(x) f(x) X n n f(x i )P i n x n i P i X n 2 G(k) e ikx = (ik) n
More informationStudies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth
Studies of Foot Form for Footwear Design (Part 9) : Characteristics of the Foot Form of Young and Elder Women Based on their Sizes of Ball Joint Girth and Foot Breadth Akiko Yamamoto Fukuoka Women's University,
More information2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
More information「スウェーデン企業におけるワーク・ライフ・バランス調査 」報告書
1 2004 12 2005 4 5 100 25 3 1 76 2 Demoskop 2 2004 11 24 30 7 2 10 1 2005 1 31 2 4 5 2 3-1-1 3-1-1 Micromediabanken 2005 1 507 1000 55.0 2 77 50 50 /CEO 36.3 37.4 18.1 3-2-1 43.0 34.4 / 17.6 3-2-2 78 79.4
More information4.1 % 7.5 %
2018 (412837) 4.1 % 7.5 % Abstract Recently, various methods for improving computial performance have been proposed. One of these various methods is Multi-core. Multi-core can execute processes in parallel
More information塗装深み感の要因解析
17 Analysis of Factors for Paint Depth Feeling Takashi Wada, Mikiko Kawasumi, Taka-aki Suzuki ( ) ( ) ( ) The appearance and quality of objects are controlled by paint coatings on the surfaces of the objects.
More information第 55 回自動制御連合講演会 2012 年 11 月 17 日,18 日京都大学 1K403 ( ) Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. T
第 55 回自動制御連合講演会 212 年 11 月 日, 日京都大学 1K43 () Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. Tokumoto, T. Namerikawa (Keio Univ. ) Abstract The purpose of
More information,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,.,
J. of Population Problems. pp.,.,,,.,,..,,..,,,,.,.,,...,.,,..,.,,,. ,,.,,.,..,.,,,.,, Aldous,.,,.,,.,,, NPO,,.,,,,,,.,,,,.,,,,..,,,,., ,,.,,..,,.,.,.,,,,,.,.,.,,,. European Labour Force Survey,,.,,,,,,,
More information25 II :30 16:00 (1),. Do not open this problem booklet until the start of the examination is announced. (2) 3.. Answer the following 3 proble
25 II 25 2 6 13:30 16:00 (1),. Do not open this problem boolet until the start of the examination is announced. (2) 3.. Answer the following 3 problems. Use the designated answer sheet for each problem.
More information09‘o’–
Gerald Graff s Method of Teaching Writing to First-Year College Students: Toward an Argument Culture IZUMI, Junji Abstract It is not easy to teach today s college students how to argue. Building on over
More informationわが国企業による資金調達方法の選択問題
* takeshi.shimatani@boj.or.jp ** kawai@ml.me.titech.ac.jp *** naohiko.baba@boj.or.jp No.05-J-3 2005 3 103-8660 30 No.05-J-3 2005 3 1990 * E-mailtakeshi.shimatani@boj.or.jp ** E-mailkawai@ml.me.titech.ac.jp
More informationVol. 36, Special Issue, S 3 S 18 (2015) PK Phase I Introduction to Pharmacokinetic Analysis Focus on Phase I Study 1 2 Kazuro Ikawa 1 and Jun Tanaka 2
Vol. 36, Special Issue, S 3 S 18 (2015) PK Phase I Introduction to Pharmacokinetic Analysis Focus on Phase I Study 1 2 Kazuro Ikawa 1 and Jun Tanaka 2 1 2 1 Department of Clinical Pharmacotherapy, Hiroshima
More informationIPSJ-TOM
Vol. 2 No. 2 47 57 (Mar. 2009) 1, 2 1 3 1 Web Performance Evaluation of Recommendation Algorithms Based on Rating-recommendation Interaction Akihiro Yamashita, 1, 2 Hidenori Kawamura, 1 Hiroyuki Iizuka
More informationA Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, Sc
A Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, School of Medicine, Tokushima University, Tokushima Fetal
More information..,,,, , ( ) 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
More informationVariational Auto Encoder
Variational Auto Encoder nzw 216 年 12 月 1 日 1 はじめに 深層学習における生成モデルとして Generative Adversarial Nets (GAN) と Variational Auto Encoder (VAE) [1] が主な手法として知られている. 本資料では,VAE を紹介する. 本資料は, 提案論文 [1] とチュートリアル資料 [2]
More informationO x y z O ( O ) O (O ) 3 x y z O O x v t = t = 0 ( 1 ) O t = 0 c t r = ct P (x, y, z) r 2 = x 2 + y 2 + z 2 (t, x, y, z) (ct) 2 x 2 y 2 z 2 = 0
9 O y O ( O ) O (O ) 3 y O O v t = t = 0 ( ) O t = 0 t r = t P (, y, ) r = + y + (t,, y, ) (t) y = 0 () ( )O O t (t ) y = 0 () (t) y = (t ) y = 0 (3) O O v O O v O O O y y O O v P(, y,, t) t (, y,, t )
More informationkubo2015ngt6 p.2 ( ( (MLE 8 y i L(q q log L(q q 0 ˆq log L(q / q = 0 q ˆq = = = * ˆq = 0.46 ( 8 y 0.46 y y y i kubo (ht
kubo2015ngt6 p.1 2015 (6 MCMC kubo@ees.hokudai.ac.jp, @KuboBook http://goo.gl/m8hsbm 1 ( 2 3 4 5 JAGS : 2015 05 18 16:48 kubo (http://goo.gl/m8hsbm 2015 (6 1 / 70 kubo (http://goo.gl/m8hsbm 2015 (6 2 /
More information[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
More informationThe 18th Game Programming Workshop ,a) 1,b) 1,c) 2,d) 1,e) 1,f) Adapting One-Player Mahjong Players to Four-Player Mahjong
1 4 1,a) 1,b) 1,c) 2,d) 1,e) 1,f) 4 1 1 4 1 4 4 1 4 Adapting One-Player Mahjong Players to Four-Player Mahjong by Recognizing Folding Situations Naoki Mizukami 1,a) Ryotaro Nakahari 1,b) Akira Ura 1,c)
More informationQ [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]
1,a) 2,3,b) Q ϵ- 3 4 Q greedy 3 ϵ- 4 ϵ- Comparation of Methods for Choosing Actions in Werewolf Game Agents Tianhe Wang 1,a) Tomoyuki Kaneko 2,3,b) Abstract: Werewolf, also known as Mafia, is a kind of
More information1 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,
More information01.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
More informationAR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
More informationTable 1 Experimental conditions Fig. 1 Belt sanded surface model Table 2 Factor loadings of final varimax criterion 5 6
JSPE-54-04 Factor Analysis of Relationhsip between One's Visual Estimation and Three Dimensional Surface Roughness Properties on Belt Sanded Surface Motoyoshi HASEGAWA and Masatoshi SHIRAYAMA This paper
More information0
0 1 2 3 4 5 6 7 1 12 2 1 2 3 2 1 2 n 8 1 2 e11 3 g 4 e 5 n n e16 9 e12 1 09e 2 10e 3 03e 1 2 4 e 0905e f n 10 1 1 2 2 3 3 4 4 5 6 11 1 2 12 1 E 2 JE 4 E *)*%E 5 N 3 *)!**# EG K E J N N 13 14 15 16 17 o
More information2017 (413812)
2017 (413812) Deep Learning ( NN) 2012 Google ASIC(Application Specific Integrated Circuit: IC) 10 ASIC Deep Learning TPU(Tensor Processing Unit) NN 12 20 30 Abstract Multi-layered neural network(nn) has
More information05_藤田先生_責
This report shows innovation of competency of our faculty of social welfare. The aim of evaluation competency is improvement in the Social welfare education effects, by understanding of studentʼs development
More informationkubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
More information2 ( ) 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
More informationH ( Competition ) 2 Bulmer Erickson 1971, Case 1999 Park 1954, Case 1999 Brown and Rothery Argentine ants > Harvester an
( Competition ) Bulmer 99 3 Erickson 97, Case 999 Park 9, Case 999 Brown and Rothery 99 Argentine ants > Harvester ants T. castaneum < > T. confusum n, n dn n dn n n µ n n K K dn n µ n n K µ n n n Lotka
More informationAtCoder Regular Contest 073 Editorial Kohei Morita(yosupo) A: Shiritori if python3 a, b, c = input().split() if a[len(a)-1] == b[0] and b[len(
AtCoder Regular Contest 073 Editorial Kohei Morita(yosupo) 29 4 29 A: Shiritori if python3 a, b, c = input().split() if a[len(a)-1] == b[0] and b[len(b)-1] == c[0]: print( YES ) else: print( NO ) 1 B:
More informationFAX-760CLT
FAX-760CLT ;; yy 1 f a n l p w s m t v y k u c j 09,. i 09 V X Q ( < N > O P Z R Q: W Y M S T U V 1 2 3 4 2 1 1 2 1 2 j 11 dd e i j i 1 ; 3 oo c o 1 2 3 4 5 6 j12 00 9 i 0 9 i 0 9 i 0 9 i oo
More informationLLG-R8.Nisus.pdf
d M d t = γ M H + α M d M d t M γ [ 1/ ( Oe sec) ] α γ γ = gµ B h g g µ B h / π γ g = γ = 1.76 10 [ 7 1/ ( Oe sec) ] α α = λ γ λ λ λ α γ α α H α = γ H ω ω H α α H K K H K / M 1 1 > 0 α 1 M > 0 γ α γ =
More informationkubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : :
kubostat2017c p.1 2017 (c), a generalized linear model (GLM) : kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 kubostat2017c (http://goo.gl/76c4i) 2017 (c) 2017 11 14 1 / 47 agenda
More information( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
More information