Entry Furuhashi:1995:NAG from lncs1995b.bib
Last update: Sun Oct 15 02:35:30 MDT 2017
Top |
Symbols |
Numbers |
Math |
A |
B |
C |
D |
E |
F |
G |
H |
I |
J |
K |
L |
M |
N |
O |
P |
Q |
R |
S |
T |
U |
V |
W |
X |
Y |
Z
BibTeX entry
@Article{Furuhashi:1995:NAG,
author = "T. Furuhashi and Y. Miyata and K. Nakaoka and Y.
Uchikawa",
title = "A New Approach to Genetic Based Machine Learning and
an Efficient Finding of Fuzzy Rules --- Proposal of
Nagoya Approach",
journal = j-LECT-NOTES-COMP-SCI,
volume = "1011",
pages = "173--??",
year = "1995",
CODEN = "LNCSD9",
ISSN = "0302-9743 (print), 1611-3349 (electronic)",
ISSN-L = "0302-9743",
bibdate = "Sat May 11 13:45:32 MDT 1996",
bibsource = "http://www.math.utah.edu/pub/tex/bib/lncs1995b.bib",
acknowledgement = ack-nhfb,
}
Related entries
- approach,
954(0)32,
954(0)105,
954(0)173,
956(0)17,
956(0)49,
956(0)236,
957(0)172,
957(0)199,
959(0)91,
964(0)477,
965(0)424,
967(0)5,
968(0)276,
969(0)349,
969(0)574,
970(0)114,
970(0)246,
970(0)588,
970(0)759,
970(0)796,
970(0)950,
972(0)318,
974(0)17,
974(0)62,
974(0)121,
974(0)139,
974(0)145,
974(0)594,
975(0)419,
976(0)463,
978(0)48,
978(0)293,
978(0)522,
979(0)227,
979(0)494,
980(0)285,
983(0)21,
984(0)18,
984(0)223,
987(0)278,
990(0)107,
990(0)153,
990(0)285,
990(0)323,
990(0)419,
991(0)219,
991(0)232,
991(0)272,
991(0)473,
992(0)163,
992(0)175,
992(0)231,
992(0)335,
993(0)166,
996(0)1,
998(0)40,
998(0)347,
998(0)360,
1006(0)92,
1006(0)214,
1010(0)33,
1010(0)121,
1010(0)265,
1010(0)520,
1013(0)1,
1013(0)19,
1013(0)378,
1017(0)101,
1018(0)258,
1020(0)91,
1021(0)65,
1021(0)101,
1021(0)308,
1021(0)355,
1021(0)434,
1024(0)173,
1024(0)181,
1024(0)427,
1024(0)507
- based,
954(0)173,
956(0)127,
956(0)155,
956(0)201,
958(0)209,
958(0)224,
959(0)264,
959(0)650,
961(0)316,
963(0)262,
963(0)297,
968(0)332,
970(0)286,
970(0)310,
970(0)532,
970(0)568,
970(0)674,
970(0)692,
970(0)932,
973(0)392,
973(0)418,
974(0)77,
974(0)97,
974(0)121,
974(0)145,
974(0)153,
974(0)159,
974(0)367,
974(0)387,
974(0)411,
974(0)429,
974(0)453,
974(0)588,
974(0)677,
974(0)697,
975(0)86,
975(0)104,
975(0)139,
975(0)158,
976(0)186,
978(0)293,
978(0)415,
981(0)221,
984(0)75,
986(0)283,
988(0)311,
989(0)254,
990(0)95,
991(0)141,
991(0)161,
991(0)219,
992(0)231,
992(0)279,
993(0)40,
994(0)139,
998(0)292,
998(0)458,
998(0)495,
998(0)506,
999(0)509,
1006(0)178,
1006(0)265,
1008(0)242,
1008(0)329,
1010(0)1,
1010(0)205,
1010(0)371,
1010(0)481,
1011(0)56,
1012(0)363,
1013(0)503,
1019(0)17,
1020(0)17,
1020(0)134,
1021(0)308,
1022(0)65,
1023(0)254,
1024(0)215,
1024(0)323,
1024(0)355,
1024(0)427,
1024(0)497,
1024(0)501,
1024(0)512,
1024(0)526,
1025(0)2,
1025(0)84,
1025(0)100,
1025(0)237
- Efficient,
955(0)171,
959(0)31,
959(0)71,
959(0)313,
959(0)597,
962(0)393,
963(0)84,
963(0)311,
963(0)438,
964(0)36,
965(0)146,
966(0)181,
966(0)301,
966(0)367,
966(0)379,
966(0)527,
966(0)715,
969(0)211,
969(0)247,
970(0)25,
970(0)424,
971(0)340,
972(0)303,
974(0)45,
976(0)515,
978(0)584,
979(0)17,
979(0)238,
979(0)420,
979(0)523,
980(0)115,
981(0)101,
982(0)241,
982(0)259,
983(0)33,
983(0)261,
990(0)397,
997(0)25,
998(0)501,
1000(0)62,
1004(0)234,
1009(0)270,
1012(0)399,
1013(0)91,
1013(0)281,
1013(0)485,
1017(0)290,
1018(0)318,
1019(0)111,
1023(0)48,
1023(0)225,
1024(0)292,
1025(0)184,
1026(0)37
- Finding,
959(0)131,
959(0)472,
970(0)850,
974(0)191,
979(0)280,
1004(0)102,
1004(0)112,
1004(0)332,
1004(0)352,
1004(0)382,
1012(0)385,
1017(0)14,
1025(0)205
- fuzzy,
970(0)574,
973(0)257,
974(0)281,
974(0)703,
978(0)124,
978(0)385,
992(0)25,
992(0)37,
1000(0)536,
1010(0)510,
1011(0)1,
1011(0)19,
1011(0)34,
1011(0)48,
1011(0)56,
1011(0)85,
1011(0)101,
1011(0)148,
1012(0)31,
1012(0)413,
1012(0)431,
1024(0)258,
1024(0)507
- genetic,
956(0)17,
956(0)28,
956(0)41,
956(0)109,
956(0)127,
956(0)155,
956(0)166,
956(0)187,
956(0)305,
970(0)766,
973(0)563,
974(0)121,
974(0)563,
975(0)352,
980(0)317,
992(0)353,
993(0)1,
993(0)14,
993(0)94,
993(0)154,
993(0)181,
993(0)224,
993(0)234,
1011(0)111,
1011(0)127,
1011(0)148,
1023(0)254
- learning,
954(0)188,
956(0)17,
958(0)224,
959(0)334,
961(0)1,
961(0)49,
961(0)63,
961(0)146,
961(0)155,
961(0)190,
961(0)292,
961(0)316,
961(0)340,
961(0)363,
961(0)391,
970(0)432,
974(0)287,
981(0)113,
984(0)60,
984(0)151,
990(0)129,
990(0)165,
990(0)397,
991(0)121,
991(0)141,
991(0)181,
991(0)201,
991(0)242,
992(0)44,
992(0)56,
992(0)353,
992(0)377,
992(0)395,
993(0)103,
993(0)200,
997(0)25,
997(0)41,
997(0)55,
997(0)66,
997(0)95,
997(0)110,
997(0)123,
997(0)138,
997(0)169,
997(0)228,
997(0)239,
997(0)249,
1000(0)518,
1004(0)171,
1010(0)98,
1010(0)109,
1010(0)229,
1010(0)301,
1010(0)431,
1010(0)491,
1010(0)559,
1011(0)127,
1012(0)493,
1020(0)91
- machine,
955(0)482,
956(0)257,
959(0)282,
959(0)607,
961(0)108,
961(0)146,
962(0)72,
964(0)258,
964(0)356,
965(0)374,
966(0)501,
967(0)315,
969(0)247,
969(0)309,
973(0)427,
975(0)68,
975(0)282,
979(0)89,
980(0)121,
980(0)349,
982(0)151,
987(0)146,
997(0)153,
997(0)282,
1000(0)101,
1000(0)518,
1010(0)98,
1010(0)441,
1012(0)1
- new,
954(0)202,
955(0)13,
955(0)334,
959(0)91,
959(0)292,
963(0)15,
964(0)314,
968(0)124,
969(0)106,
969(0)349,
970(0)246,
970(0)392,
970(0)472,
970(0)509,
970(0)594,
970(0)674,
971(0)324,
973(0)195,
974(0)17,
974(0)121,
974(0)197,
974(0)229,
974(0)341,
974(0)381,
975(0)1,
977(0)180,
978(0)217,
980(0)99,
980(0)285,
982(0)81,
990(0)17,
990(0)29,
990(0)57,
990(0)249,
990(0)359,
991(0)39,
991(0)121,
997(0)1,
1012(0)315,
1013(0)411,
1015(0)1,
1018(0)84,
1018(0)228,
1018(0)258,
1024(0)233,
1025(0)205
- proposal,
970(0)790,
974(0)405,
992(0)389
- rules,
966(0)327,
985(0)182,
985(0)197,
985(0)230,
990(0)311,
991(0)181,
1010(0)325,
1011(0)101,
1011(0)127,
1013(0)19,
1013(0)55