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1 RWC 10 RWC RWC 21 RWC 1. RWC (Real World Computing ) ( ) RWCP RWC (RWI) (PDC) 2 RWC (5G) 1

2 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 III VI) ( ) ( [2][3]) ( ) 1) RWC 2

3 OCR ASPET/70, 71 2) 60 (PIPS) : TSS ) 10 Lie [4] [5] (HLAC) 1981 [6] 10 Bayes 2 Boole 1 4) 70 (AI) (If-then rule) (KE) 2 AI KE 1969 ARPA-net 70 2) 3) 4) A 3

4 1: 80 TCP/IP ) AI KE AI (5G) ( ) ICOT PIM OS ( ) NRC [8] [11] From Boolean to Bayesian 6) [9][10] 80 2 AI KE 5G PIM 70 5) 6) Zadeh 1994 Soft Computing [23] 4

5 ( 2 ) 2: : AI 5

6 [37] AI 1956 McCarthy Minsky Artificial Intelligence Newell AI Logic Theorist ELIZA AI 7) McCarthy, Hayes 1972 NP AI AI 70 AI MYCIN DENDRAL Feigenbaum AI KE AI If A, then B AI McCulloch-Pitts, 1943 Hebb, 1949 Perceptron Rosenblatt, ) AI 6

7 Widrow-Hoff, 1960, Minsky Papert Perceptrons 80 Hinton, 1984 Hopfield, 1985 Rumelhart, 1986 AI PDP (Parallel Distributed Processing) Pearl, Chow, Widrow-Hoff Fisher Koford-Groner, 1966 Patterson-Womack, 1966 Fisher 1972, 1981 [6] A : 80 AI 7

8 AI 80 Toy world Real world Well-defined Ill-defined 3. RWC G

9 RWC ( ) [12] 1991 [13] NIPT (New Information Processing Technology) RWC (Real World Computing) ) [12, 13, 15, 17, 18, 21] 21 8) 5G 3D [16] 9

10 / / subsymbolic : : :

11 4: 1 [14, 15, 16, 17, 18, 19, 21, 22] 5G 5G

12 GA 9) RWC RWC 5 5: RWC [13] ( ) 9) Genetic Algorithm : 12

13 4. RWC RWC (RWCP: RWC Partnership) 10 (TRC) TRC RWC RWC TRC TRC RWC , H4 H8 RWCP GMD ISS SNN SICS 4 7 (GMD ) (ISS KRDL ) (SNN) (SICS) RWC NIPT 10) 67 24,

14 1: [35] RWCP H4 H5 H6 H7 H8 TRC OS NTT MRI NEC NEC MRI NEC LD OBIS ISS SNN SICS GMD GMD SICS ISS 14

15 11) JOP RWC 2 34 RISC-LINZ IRST 1994 [35] ETL RWC RWCP TRC 2 RWC TRC RWC 2 [20] 2: ETL-RWC H4 H5 H6 H7 H8 EM-X OS RWCP RWCP Joint Symposium 500 RWC 11) NIPT AI 15

16 [24] [25] [35] RWC RWCP 12) TRC RWC TRC RWC-1 OB RWC [3] RWC , H TRC RWC RWI PDC 12) 16

17 RWC 13) TRC [28] [31] RWIC 1997 RWC , H9 H13 TRC RWIC 6 RWI RWCP [31][32][33] 6: [35] 13) TRC 17

18 RWI 7 [29][31] 7: [35] RWIC RWC RWCP TRC 18

19 PDC 14) 8 [30][31] 8: PDC [35] TRC 3 RWIC 4 RWCP 3: RWI RWI RWC 14) 2000 H12 NEDO 19

20 4: RWCP RWCP RWI TRC Cross Madiator Cross Mediator NTT MRI NEC NEC ISS KRDL ISS KRDL SICS SNN SNN GMD SICS RWCP PDC TRC PAPIA NEC NEC LAN LD MRI GMD 20

21 RWI PDC TRC PC TRC RWI 19 PDC 11 [35] 5. [21] [18] [19] RWC 92 [16] RWC 94, 95, 97, 98, 00, 01 RWCP RWC NEWS [1] RWIC [38] [35] RWC RWC ) (GA) EM-X RWC-1 PC RWC-1 RWC RWC NEWS [26] RWC [27] [28] RWI PDC TRC [29][30] 15) SF HAL

22 5.. 2 [1] [35] RWI (Jijo2) BAYONET CrossMediator PDC RHiNET SCore Omni OpenMP PAPIA RWC ,000 RWC NEWS Vol. 20 ( ) [34] 10 RWC [35] [36] B RWCP RWIC PWC TRC RWI RWC RWC 6 22

23 RWC 21 COE [40] 2001 RWC 13 HLAC CHLAC 16) RWC TRC RWCP PC 1024 cpu RWC RWI RWC AI Deep Learnig Google IT AI RWI ) AI RWI [39] RWC-RWI 6. RWC ICT iphone 16) RWC 17) RWC 23

24 RWC AI RWC RWI ICT RWC NEDO 18) RWC AI RWC RWC Web RWCP [1] RWCP RWI 18) RWC 24

25 [1] RWCP, 10 [13, 31, 35, 36] RWC NEWS. (http://keima.la.coocan.jp/rwcp/memorial/index.html) [2],. (http://museum.ipsj.or.jp/computer/dawn/index.html) [3] :,, IPSJ Magazine, Vol. 44, No. 10 (Oct. 2003). (http://museum.ipsj.or.jp/guide/pdf/magazine/ipsj-mgn pdf) [4] N. Otsu: An Invariant Theory of Linear Functionals as Linear Feature Extractors, Bull. ETL, Vol. 37, No. 10, pp (1973). [5] N. Otsu: Nonlinear Discriminant Analysis as a Natural Extension of the Linear Case, Behaviormetrika, Vol. 2, pp (1975). [6] :,, 818, 210 (1981). [7] N. Otsu: Optimal Linear and Nonlinear Solutions for Least-square Discriminant Feature Extraction, Proc. of 6th Int. Conf. on Pattern Recognition, pp (1982). [8],,,,, :,, 211, 136 (1985). [9] :,, Vol. 71, No. 11, pp (1988). [10] N. Otsu: Toward Soft Logic for the Foundation of Felxible Information Processing, Bull. ETL, Vol. 53, No. 10, pp (1989). [11],, :,, 5, pp , (1990). [12], (Mar. 1991). [13], (May, 1992) in [35]. [14] N. Otsu: Toward Flexible Information Processing: Theory and Novel Functions, Japan Computer Quarterly, JIPDEC, No. 89, pp (1992). [15] :,, Computer Today 5 No.49 (1992). [16] : RWC 92,, Vol. 25, No. 6 (1992). [17],, 4 (1993). [18] N. Otsu: Toward Flexible Intelligence MITI s New Program of Real World Computing, invited paper, Proc. IJCAI-93, Vol. 1, pp (Chambery, 1993). [19] N. Otsu: Real World Computing Program Overview, Theory and Novel Functions, Proc. IJCNN-93, pp (Nagoya, 1993). [20] : RWC,, Vol. 12 (1993). [21] :,, RWC, Vol. 34, No. 12 (1993). [22] :,, Vol. 9, No. 5, pp (1994). [23] Zadeh: Fuzzy Logic, Neural Networks, and Soft Computing, Communication of the ACM, Vol. 37, No. 3, pp (March 1994). [24] : RWC, RWC NEWS, Vol. 1 (Apr. 1995). [25] : RWC, RWC NEWS, Vol. 2 (Jul. 1995). [26] RWC 1997 (Jun. 1997), in RWC NEWS, Vol. 8 (May. 1997). [27], RWC 1997, ibid (1997). 25

26 [28] :, RWC 1997, ibid (1997). [29] :, RWC 1997, ibid (1997). [30] :, RWC 1997, ibid (1997). [31] (RWC-RWI/PDC), (May, 1997) in [35]. [32] (RWC-RWI/PDC),, RWC NEWS, Vol. 9 (Aug. 1997). [33],, : RWC, RWC NEWS, Vol. 10 (Nov. 1997). [34] RWC 2001 (Oct. 2001), in RWC NEWS, Vol. 20 (Nov. 2001). [35], (Feb. 2002) at [1]. [36], WG (Feb. 2002) at [1]. [37] :,, Vol. 122, No. 4, pp (2002). [38] : RWC,, Vol. 17, No. 2 (2002). [39] :, (https://staff.aist.go.jp/y.motomura/bn2002/paper/otsu.pdf) (2002). [40],, 292, (2004). A: 1970 f(r) C j y i n n y Y p(y C j ) P (C j ) (y) P (C j y) =P (C j )p(y C j )/p(y) Chow, 1957 f 26

27 f x x = Φ[f] T (λ) Φ inv [T (λ)f] =Φ inv [f] Lie ξ = g(r)f(r) dr g ξ x i λ λ =Φ var [T (λ)f] Φ var [f] f T (λ) [4][6] T (λ)f X x x m X n <m Y y = Ψ(x) Ψ Y X Ψ m n A y = A x A A Y Y B Y, W Y Ψ J[Ψ] = tr (W 1 Y B Y ) X B X, W X A B X a i = λ i W X a i n K n min(k 1,m) Fisher Fisher, y =Ψ D(x) = K P (C j x)c j j=1 27

28 Y c j X K K S =[s ij ], s ij = P (C j x)p(x C i ) dx = P (C j C i ) n n K 1 [5][6] p(x C j ) Y K 1 ε 2 [Ψ] = K j=1 P (C j ) Ψ(x) e j 2 p(x C j ) dx Y e j (j 1 e i e j = δ ij ) C j δε 2 [Ψ] = 2 K j=1 P (C j ){Ψ(x) e j }p(x C j ) δψ(x) dx =0 δψ(x) [6][7] K y =Ψ P (C j )p(x C j ) K R(x) = e j = P (C j x) e j p(x) j=1 e j ε 2 [Ψ] 1986 Ψ R (x) ε 2 [Ψ R]=1 tr Γ Γ =[γ ij ] K K γ ij = P (C i x)p (C j x)p(x)dx = P (C i C j ) s ij j=1 P (C i )s ij = P (C i )P (C j C i )=P (C i C j )=γ ij S Γ P (C i C i ) 0 D M E[M] = D M 2 + kρ(m) min for M ρ(m) k 28

29 e E[M] = e D M 2 e kρ(m) max for M p(d M) p(m) e E[M] = p(d M)p(M) =p(m D)p(D) max for M D p(m D) M B: [36] RWI 19) RWCP RWCP RWI. 19) RWI

30 GA RHiNET SCore MPEG7 [36] ( ) RWC ( )

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