tex/gostyle.bib

   1 % This file was created with JabRef 2.6b2.
   2 % Encoding: UTF-8
   3
   4 @ELECTRONIC{GoR,
   5   author = {Ales Cieply and others},
   6   title = {EGF ratings system -- System description},
   7   owner = {pasky},
   8   timestamp = {2011.03.10},
   9   url = {http://www.europeangodatabase.eu/EGD/EGF_rating_system.php}
  10 }
  11
  12 @ELECTRONIC{ProGoR,
  13   author = {Ales Cieply and others},
  14   title = {Go Ratings of Professional and Strong Amateur Players},
  15   owner = {pasky},
  16   url = {http://www.goweb.cz/progor}
  17 }
  18
  19 @ELECTRONIC{KGSAnalytics,
  20   author = {Kazuhiro},
  21   title = {KGS Analytics},
  22   owner = {pasky},
  23   url = {http://kgs.gosquares.net/}
  24 }
  25
  26 @ELECTRONIC{Kombilo,
  27   author = {Ulrich G\"ortz},
  28   title = {Kombilo --- a go database program},
  29   owner = {pasky},
  30   url = {http://www.u-go.net/kombilo/}
  31 }
  32
  33 @ELECTRONIC{MoyoGo,
  34   author = {Frank de Groot},
  35   title = {Moyo Go Studio},
  36   owner = {pasky},
  37   url = {http://www.moyogo.com/}
  38 }
  39
  40 @ELECTRONIC{SGF,
  41   author = {Arno Hollosi},
  42   title = {SGF File Format},
  43   owner = {pasky},
  44   url = {http://www.red-bean.com/sgf/}
  45 }
  46
  47 @ELECTRONIC{GTP,
  48   author = {Gunnar Farneb\"{a}ck},
  49   title = {GTP --- Go Text Protocol},
  50   owner = {pasky},
  51   url = {http://www.lysator.liu.se/~gunnar/gtp/}
  52 }
  53
  54 @ELECTRONIC{RankComparison,
  55   author = {Sensei's Library},
  56   title = {Rank --- worldwide comparison},
  57   owner = {pasky},
  58   url = {http://senseis.xmp.net/?RankWorldwideComparison}
  59 }
  60
  61 @ELECTRONIC{GTL,
  62   author = {Jean-loup Gailly, Bill Hosken and others},
  63   title = {The Go Teaching Ladder},
  64   owner = {pasky},
  65   url = {http://gtl.xmp.net/}
  66 }
  67
  68 @ELECTRONIC{GoGoD,
  69   author = {T. Mark Hall, John Fairbairn},
  70   title = {Games of Go on Disk --- GoGoD Encyclopaedia and Database},
  71   owner = {pasky},
  72   url = {http://www.gogod.co.uk/}
  73 }
  74
  75 @ELECTRONIC{GoDiscThread,
  76   author = {GoDiscussions},
  77   title = {Pro Styles (discussion thread)},
  78   owner = {pasky},
  79   url = {http://www.godiscussions.com/forum/showthread.php?t=10980}
  80 }
  81
  82 @ELECTRONIC{Pachi,
  83   author = {Petr Baudi\v{s} and others},
  84   title = {Pachi --- Simple Go/Baduk/Weiqi Bot},
  85   owner = {pasky},
  86   url = {http://repo.or.cz/w/pachi.git}
  87 }
  88
  89 @ELECTRONIC{GoStyle,
  90   author = {Josef Moud\v{r}\'{i}k, Petr Baudi\v{s} and others},
  91   title = {GoStyle --- Determine playing style in the game of Go},
  92   owner = {pasky},
  93   url = {http://repo.or.cz/w/gostyle.git}
  94 }
  95
  96 @ELECTRONIC{TPA,
  97   author = {{\relax QED} GROUP},
  98   title = {Team Profile Analyzer},
  99   owner = {pasky},
 100   url = {http://www.teamprofileanalyzer.com/}
 101 }
 102
 103 @ELECTRONIC{SociomapSite,
 104   author = {{\relax QED} GROUP},
 105   title = {Sociomapping Suite},
 106   owner = {pasky},
 107   url = {http://www.sociomap.com/}
 108 }
 109
 110 @book{Sociomaps,
 111         author = {{H}\"{o}schl, {C}yril},
 112         title = {Visualization of Sociomaps},
 113         note = {{Bachelor Thesis}},
 114         year = {2006},
 115         location = {Praha},
 116         publisher = {MFF UK},
 117 }
 118
 119 @inproceedings{SpatPat,
 120         author = {Stern, David and Herbrich, Ralf and Graepel, Thore},
 121         title = {Bayesian pattern ranking for move prediction in the game of Go},
 122         booktitle = {ICML '06: Proceedings of the 23rd international conference on Machine learning},
 123         year = {2006},
 124         isbn = {1-59593-383-2},
 125         pages = {873--880},
 126         location = {Pittsburgh, Pennsylvania},
 127         doi = {http://doi.acm.org/10.1145/1143844.1143954},
 128         publisher = {ACM},
 129         address = {New York, NY, USA},
 130 }
 131
 132 @inproceedings{PatElo,
 133         title = { {C}omputing {E}lo {R}atings of {M}ove {P}atterns in the {G}ame of {G}o},
 134         author = {{C}oulom, {R}{\'e}mi},
 135         abstract = {{M}ove patterns are an essential method to incorporate domain knowledge into {G}o-playing programs. {T}his paper presents a new {B}ayesian technique for supervised learning of such patterns from game records, based on a generalization of {E}lo ratings. {E}ach sample move in the training data is considered as a victory of a team of pattern features. {E}lo ratings of individual pattern features are computed from these victories, and can be used in previously unseen positions to compute a probability distribution over legal moves. {I}n this approach, several pattern features may be combined, without an exponential cost in the number of features. {D}espite a very small number of training games (652), this algorithm outperforms most previous pattern-learning algorithms, both in terms of mean log-evidence (−2.69), and prediction rate (34.9%). {A} 19x19 {M}onte-{C}arlo program improved with these patterns reached the level of the strongest classical programs.},
 136         affiliation = {{SEQUEL} - {INRIA} {F}uturs - {INRIA} - {CNRS} : {UMR}8022 - {CNRS} : {UMR}8146 - {U}niversit{\'e} des {S}ciences et {T}echnologies de {L}ille - {L}ille {I} - {U}niversit{\'e} {C}harles de {G}aulle - {L}ille {III} - {E}cole {C}entrale de {L}ille },
 137         booktitle = {{C}omputer {G}ames {W}orkshop },
 138         address = {{A}msterdam {P}ays-{B}as },
 139         editor = {van den {H}erik, {H}. {J}aap and {M}ark {W}inands and {J}os {U}iterwijk and {M}aarten {S}chadd },
 140         audience = {non sp{\'e}cifi{\'e}e },
 141         year = {2007},
 142         URL = {http://hal.inria.fr/inria-00149859/en/},
 143         URL = {http://hal.inria.fr/inria-00149859/PDF/MMGoPatterns.pdf},
 144 }
 145         note = {{I}.: {C}omputing {M}ethodologies/{I}.2: {ARTIFICIAL} {INTELLIGENCE}/{I}.2.6: {L}earning, {I}.: {C}omputing {M}ethodologies/{I}.2: {ARTIFICIAL} {INTELLIGENCE}/{I}.2.8: {P}roblem {S}olving, {C}ontrol {M}ethods, and {S}earch/{I}.2.8.3: {G}raph and tree search strategies },
 146
 147 @ARTICLE{CoverHart1967,
 148   author = {Thomas M. Cover and Peter E. Hart},
 149   title = {Nearest neighbor pattern classification},
 150   journal = {IEEE Transactions on Information Theory},
 151   year = {1967},
 152   volume = {13},
 153   pages = {21-27},
 154   number = {1},
 155   owner = {hellboy},
 156   timestamp = {2010.03.10}
 157 }
 158
 159 @INPROCEEDINGS{GellySilver2008,
 160   author = {Gelly, Sylvain and Silver, David},
 161   title = {Achieving master level play in 9x9 computer go},
 162   booktitle = {AAAI'08: Proceedings of the 23rd national conference on Artificial
 163         intelligence},
 164   year = {2008},
 165   pages = {1537--1540},
 166   publisher = {AAAI Press},
 167   abstract = {The UCT algorithm uses Monte-Carlo simulation to estimate the value
 168         of states in a search tree from the current state. However, the first
 169         time a state is encountered, UCT has no knowledge, and is unable
 170         to generalise from previous experience. We describe two extensions
 171         that address these weaknesses. Our first algorithm, heuristic UCT,
 172         incorporates prior knowledge in the form of a value function. The
 173         value function can be learned offline, using a linear combination
 174         of a million binary features, with weights trained by temporal-difference
 175         learning. Our second algorithm, UCT-RAVE, forms a rapid online generalisation
 176         based on the value of moves. We applied our algorithms to the domain
 177         of 9 × 9 Computer Go, using the program MoGo. Using both heuristic
 178         UCT and RAVE, MoGo became the first program to achieve human master
 179         level in competitive play.},
 180   isbn = {978-1-57735-368-3},
 181   location = {Chicago, Illinois}
 182 }
 183
 184 @BOOK{Jolliffe1986,
 185   title = {Principal Component Analysis},
 186   publisher = {Springer, New York},
 187   year = {1986},
 188   author = {I.T. Jolliffe},
 189   owner = {hellboy}
 190 }
 191
 192 @BOOK{Elo,
 193   title = {The rating of chessplayers, past and present},
 194   publisher = {Arco, New York},
 195   year = {1978},
 196   author = {Arpad E. Elo},
 197   isbn = {0668047216},
 198   owner = {pasky}
 199 }
 200
 201 @ELECTRONIC{KohonenPy,
 202   author = {lmjohns3},
 203   title = {python-kohonen, {A} library of {Kohonen} maps},
 204   howpublished = {Released under {MIT} License},
 205   url = {http://code.google.com/p/python-kohonen/},
 206   owner = {hellboy},
 207   timestamp = {2010.03.10}
 208 }
 209
 210 @TECHREPORT{Nissen2003,
 211   author = {S. Nissen},
 212   title = {Implementation of a Fast Artificial Neural Network Library (fann)},
 213   institution = {Department of Computer Science University of Copenhagen (DIKU)},
 214   year = {2003},
 215   note = {http://fann.sf.net},
 216   owner = {hellboy},
 217   timestamp = {2010.03.10}
 218 }
 219
 220 @ARTICLE{Pearson,
 221   author = {J. L. Rodgers and W. A. Nicewander},
 222   title = {{Thirteen ways to look at the correlation coefficient}},
 223   journal = {The American Statistician},
 224   year = {1988},
 225   month = {Feb},
 226   volume = {42},
 227   number = {1},
 228   pages = {59--66},
 229   owner = {pasky}
 230 }
 231
 232 @ELECTRONIC{Python25,
 233   author = {{Python Software Foundation}},
 234   month = {September},
 235   year = {2006},
 236   title = {Python 2.5},
 237   url = {http://www.python.org/dev/peps/pep-0356/},
 238   owner = {hellboy},
 239   timestamp = {2009.04.29}
 240 }
 241
 242 @INPROCEEDINGS{Riedmiller1993,
 243   author = {Martin Riedmiller and Heinrich Braun},
 244   title = {{A Direct Adaptive Method for Faster Backpropagation Learning: The
 245         RPROP Algorithm}},
 246   booktitle = {IEEE International Conference on Neural Networks},
 247   year = {1993},
 248   pages = {586--591},
 249   owner = {hellboy},
 250   timestamp = {2010.03.07}
 251 }
 252
 253 @ARTICLE{MDP,
 254   author = {Zito Tiziano and Wilbert Niko and Wiskott Laurenz and Berkes Pietro},
 255   title = {Modular toolkit for {D}ata {P}rocessing ({MDP}): a {P}ython data
 256         processing framework},
 257   journal = {Frontiers in Neuroinformatics},
 258   year = {2008},
 259   volume = {2},
 260   abstract = {Modular toolkit for Data Processing (MDP) is a data processing framework
 261         written in Python. From the user&#39;s perspective, MDP is a collection
 262         of supervised and unsupervised learning algorithms and other data
 263         processing units that can be combined into data processing sequences
 264         and more complex feed-forward network architectures. Computations
 265         are performed efficiently in terms of speed and memory requirements.
 266         From the scientific developer&#39;s perspective, MDP is a modular
 267         framework, which can easily be expanded. The implementation of new
 268         algorithms is easy and intuitive. The new implemented units are then
 269         automatically integrated with the rest of the library. MDP has been
 270         written in the context of theoretical research in neuroscience, but
 271         it has been designed to be helpful in any context where trainable
 272         data processing algorithms are used. Its simplicity on the user&#39;s
 273         side, the variety of readily available algorithms, and the reusability
 274         of the implemented units make it also a useful educational tool.},
 275   doi = {10.3389/neuro.11/008.2008},
 276   issn = {ISSN 1662-5196},
 277   owner = {hellboy},
 278   timestamp = {2010.03.05}
 279 }
 280
 281 @BOOK{Bayes,
 282         author = {{Duda}, R.~O. and {Hart}, P.~E.},
 283         title = {Pattern classification and scene analysis},
 284         publisher = {A Wiley-Interscience Publication, New York: Wiley},
 285         year = 1973
 286 }
 287
 288
 289 @comment{jabref-meta: selector_publisher:}
 290
 291 @comment{jabref-meta: selector_author:}
 292
 293 @comment{jabref-meta: selector_journal:}
 294
 295 @comment{jabref-meta: selector_keywords:}
 296