tex/gostyle.bib

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   3
   4 @ELECTRONIC{Pachi,
   5   author = {{B}audi\v{s}, {P}etr and others},
   6   title = {Pachi --- Simple Go/Baduk/Weiqi Bot},
   7   url = {http://repo.or.cz/w/pachi.git},
   8   owner = {pasky}
   9 }
  10
  11 @ELECTRONIC{GoR,
  12   author = {Ales Cieply and others},
  13   title = {EGF ratings system -- System description},
  14   url = {http://www.europeangodatabase.eu/EGD/EGF_rating_system.php},
  15   owner = {pasky},
  16   timestamp = {2011.03.10}
  17 }
  18
  19 @ELECTRONIC{ProGoR,
  20   author = {Ales Cieply and others},
  21   title = {Go Ratings of Professional and Strong Amateur Players},
  22   url = {http://www.goweb.cz/progor},
  23   owner = {pasky}
  24 }
  25
  26 @ARTICLE{CoverHart1967,
  27   author = {Thomas M. Cover and Peter E. Hart},
  28   title = {Nearest neighbor pattern classification},
  29   journal = {IEEE Transactions on Information Theory},
  30   year = {1967},
  31   volume = {13},
  32   pages = {21-27},
  33   number = {1},
  34   owner = {hellboy},
  35   timestamp = {2010.03.10}
  36 }
  37
  38 @INPROCEEDINGS{PatElo,
  39   author = {{C}oulom, {R}{\'e}mi},
  40   title = { {C}omputing {E}lo {R}atings of {M}ove {P}atterns in the {G}ame of
  41         {G}o},
  42   booktitle = {{C}omputer {G}ames {W}orkshop },
  43   year = {2007},
  44   editor = {van den {H}erik, {H}. {J}aap and {M}ark {W}inands and {J}os {U}iterwijk
  45         and {M}aarten {S}chadd },
  46   address = {{A}msterdam {P}ays-{B}as },
  47   abstract = {{M}ove patterns are an essential method to incorporate domain knowledge
  48         into {G}o-playing programs. {T}his paper presents a new {B}ayesian
  49         technique for supervised learning of such patterns from game records,
  50         based on a generalization of {E}lo ratings. {E}ach sample move in
  51         the training data is considered as a victory of a team of pattern
  52         features. {E}lo ratings of individual pattern features are computed
  53         from these victories, and can be used in previously unseen positions
  54         to compute a probability distribution over legal moves. {I}n this
  55         approach, several pattern features may be combined, without an exponential
  56         cost in the number of features. {D}espite a very small number of
  57         training games (652), this algorithm outperforms most previous pattern-learning
  58         algorithms, both in terms of mean log-evidence (−2.69), and prediction
  59         rate (34.9%). {A} 19x19 {M}onte-{C}arlo program improved with these
  60         patterns reached the level of the strongest classical programs.},
  61   affiliation = {{SEQUEL} - {INRIA} {F}uturs - {INRIA} - {CNRS} : {UMR}8022 - {CNRS}
  62         : {UMR}8146 - {U}niversit{\'e} des {S}ciences et {T}echnologies de
  63         {L}ille - {L}ille {I} - {U}niversit{\'e} {C}harles de {G}aulle -
  64         {L}ille {III} - {E}cole {C}entrale de {L}ille },
  65   audience = {non sp{\'e}cifi{\'e}e },
  66   url = {http://hal.inria.fr/inria-00149859/en/}
  67 }
  68
  69 @BOOK{Bayes,
  70   title = {Pattern classification and scene analysis},
  71   publisher = {A Wiley-Interscience Publication, New York: Wiley},
  72   year = {1973},
  73   author = {{Duda}, R.~O. and {Hart}, P.~E.}
  74 }
  75
  76 @BOOK{Elo,
  77   title = {The rating of chessplayers, past and present},
  78   publisher = {Arco, New York},
  79   year = {1978},
  80   author = {Arpad E. Elo},
  81   isbn = {0668047216},
  82   owner = {pasky}
  83 }
  84
  85 @ELECTRONIC{GTP,
  86   author = {Gunnar Farneb\"{a}ck},
  87   title = {GTP --- Go Text Protocol},
  88   url = {http://www.lysator.liu.se/~gunnar/gtp/},
  89   owner = {pasky}
  90 }
  91
  92 @ELECTRONIC{Kombilo,
  93   author = {Ulrich G\"ortz},
  94   title = {Kombilo --- a go database program},
  95   url = {http://www.u-go.net/kombilo/},
  96   owner = {pasky}
  97 }
  98
  99 @INPROCEEDINGS{GellySilver2008,
 100   author = {Gelly, Sylvain and Silver, David},
 101   title = {Achieving master level play in 9x9 computer go},
 102   booktitle = {AAAI'08: Proceedings of the 23rd national conference on Artificial
 103         intelligence},
 104   year = {2008},
 105   pages = {1537--1540},
 106   publisher = {AAAI Press},
 107   abstract = {The UCT algorithm uses Monte-Carlo simulation to estimate the value
 108         of states in a search tree from the current state. However, the first
 109         time a state is encountered, UCT has no knowledge, and is unable
 110         to generalise from previous experience. We describe two extensions
 111         that address these weaknesses. Our first algorithm, heuristic UCT,
 112         incorporates prior knowledge in the form of a value function. The
 113         value function can be learned offline, using a linear combination
 114         of a million binary features, with weights trained by temporal-difference
 115         learning. Our second algorithm, UCT-RAVE, forms a rapid online generalisation
 116         based on the value of moves. We applied our algorithms to the domain
 117         of 9 × 9 Computer Go, using the program MoGo. Using both heuristic
 118         UCT and RAVE, MoGo became the first program to achieve human master
 119         level in competitive play.},
 120   isbn = {978-1-57735-368-3},
 121   location = {Chicago, Illinois}
 122 }
 123
 124 @ELECTRONIC{GoDiscThread,
 125   author = {GoDiscussions},
 126   title = {Pro Styles (discussion thread)},
 127   url = {http://www.godiscussions.com/forum/showthread.php?t=10980},
 128   owner = {pasky}
 129 }
 130
 131 @ELECTRONIC{MoyoGo,
 132   author = {Frank de Groot},
 133   title = {Moyo Go Studio},
 134   url = {http://www.moyogo.com/},
 135   owner = {pasky}
 136 }
 137
 138 @ELECTRONIC{SociomapSite,
 139   author = {{\relax QED} GROUP},
 140   title = {Sociomapping Suite},
 141   url = {http://www.sociomap.com/},
 142   owner = {pasky}
 143 }
 144
 145 @ELECTRONIC{TPA,
 146   author = {{\relax QED} GROUP},
 147   title = {Team Profile Analyzer},
 148   url = {http://www.teamprofileanalyzer.com/},
 149   owner = {pasky}
 150 }
 151
 152 @ELECTRONIC{GoGoD,
 153   author = {Hall, T. Mark and Fairbairn, John},
 154   title = {Games of Go on Disk --- {GoGoD} Encyclopaedia and Database},
 155   url = {http://www.gogod.co.uk/},
 156   owner = {pasky}
 157 }
 158
 159 @BOOK{Haykin1994,
 160   title = {Neural Networks: A Comprehensive Foundation},
 161   publisher = {Macmillan},
 162   year = {1994},
 163   author = {Haykin, S.},
 164   address = {New York},
 165   citeulike-article-id = {2429223},
 166   keywords = {neural\_nets},
 167   owner = {hellboy},
 168   posted-at = {2008-02-26 12:33:02},
 169   priority = {2},
 170   timestamp = {2010.06.13}
 171 }
 172
 173 @ELECTRONIC{SGF,
 174   author = {Arno Hollosi},
 175   title = {SGF File Format},
 176   url = {http://www.red-bean.com/sgf/},
 177   owner = {pasky}
 178 }
 179
 180 @BOOK{Sociomaps,
 181   title = {Visualization of Sociomaps},
 182   publisher = {MFF UK},
 183   year = {2006},
 184   author = {{H}\"{o}schl, {C}yril},
 185   note = {{Bachelor Thesis}},
 186   location = {Praha}
 187 }
 188
 189 @MISC{SociomapsPersonal,
 190   author = {{H}\"{o}schl, {C}yril},
 191   howpublished      =    {personal communication},
 192   year = 2010
 193 }
 194
 195 @ELECTRONIC{GTL,
 196   author = {Jean-loup Gailly, Bill Hosken and others},
 197   title = {The Go Teaching Ladder},
 198   url = {http://gtl.xmp.net/},
 199   owner = {pasky}
 200 }
 201
 202 @BOOK{Jolliffe1986,
 203   title = {Principal Component Analysis},
 204   publisher = {Springer, New York},
 205   year = {1986},
 206   author = {I.T. Jolliffe},
 207   owner = {hellboy}
 208 }
 209
 210 @ELECTRONIC{KGSAnalytics,
 211   author = {Kazuhiro},
 212   title = {KGS Analytics},
 213   url = {http://kgs.gosquares.net/},
 214   owner = {pasky}
 215 }
 216
 217 @ELECTRONIC{NaiveBayes1,
 218   author = {{K}eselj, {V}lado and {L}in, {Y}ung-chung and others},
 219   title = {{AI::NaiveBayes1} {CPAN} Module},
 220   url = {http://search.cpan.org/~vlado/AI-NaiveBayes1-1.8/},
 221   owner = {pasky}
 222 }
 223
 224 @ELECTRONIC{RankComparison,
 225   author = {Sensei's Library},
 226   title = {Rank --- worldwide comparison},
 227   url = {http://senseis.xmp.net/?RankWorldwideComparison},
 228   owner = {pasky}
 229 }
 230
 231 @ELECTRONIC{KohonenPy,
 232   author = {lmjohns3},
 233   title = {python-kohonen, {A} library of {Kohonen} maps},
 234   howpublished = {Released under {MIT} License},
 235   url = {http://code.google.com/p/python-kohonen/},
 236   owner = {hellboy},
 237   timestamp = {2010.03.10}
 238 }
 239
 240 @ELECTRONIC{GoStyle,
 241   author = {{M}oud\v{r}\'{i}k, {J}osef and {B}audi\v{s}, {P}etr},
 242   title = {GoStyle --- Determine playing style in the game of Go},
 243   url = {http://repo.or.cz/w/gostyle.git},
 244   owner = {pasky}
 245 }
 246
 247 @TECHREPORT{Nissen2003,
 248   author = {S. Nissen},
 249   title = {Implementation of a Fast Artificial Neural Network Library (fann)},
 250   institution = {Department of Computer Science University of Copenhagen (DIKU)},
 251   year = {2003},
 252   note = {http://fann.sf.net},
 253   owner = {hellboy},
 254   timestamp = {2010.03.10}
 255 }
 256
 257 @ELECTRONIC{Python25,
 258   author = {{Python Software Foundation}},
 259   month = {September},
 260   year = {2006},
 261   title = {Python 2.5},
 262   url = {http://www.python.org/dev/peps/pep-0356/},
 263   owner = {hellboy},
 264   timestamp = {2009.04.29}
 265 }
 266
 267 @INPROCEEDINGS{Riedmiller1993,
 268   author = {Martin Riedmiller and Heinrich Braun},
 269   title = {{A Direct Adaptive Method for Faster Backpropagation Learning: The
 270         RPROP Algorithm}},
 271   booktitle = {IEEE International Conference on Neural Networks},
 272   year = {1993},
 273   pages = {586--591},
 274   owner = {hellboy},
 275   timestamp = {2010.03.07}
 276 }
 277
 278 @ARTICLE{Pearson,
 279   author = {J. L. Rodgers and W. A. Nicewander},
 280   title = {{Thirteen ways to look at the correlation coefficient}},
 281   journal = {The American Statistician},
 282   year = {1988},
 283   volume = {42},
 284   pages = {59--66},
 285   number = {1},
 286   month = {Feb},
 287   owner = {pasky}
 288 }
 289
 290 @INPROCEEDINGS{SpatPat,
 291   author = {Stern, David and Herbrich, Ralf and Graepel, Thore},
 292   title = {Bayesian pattern ranking for move prediction in the game of Go},
 293   booktitle = {ICML '06: Proceedings of the 23rd international conference on Machine
 294         learning},
 295   year = {2006},
 296   pages = {873--880},
 297   address = {New York, NY, USA},
 298   publisher = {ACM},
 299   doi = {http://doi.acm.org/10.1145/1143844.1143954},
 300   isbn = {1-59593-383-2},
 301   location = {Pittsburgh, Pennsylvania}
 302 }
 303
 304 @INPROCEEDINGS{TeamProf,
 305   author = {{S}rb, {T}omas and {S}\'{y}kora, Jiri and {B}ahbouh, {R}advan and
 306         {H}\"{o}schl, {C}yril},
 307   title = {Using Visualisation of Group Structure and Dynamics in Team Development},
 308   booktitle = {50th Conference of the International Military Training Association},
 309   year = {2008},
 310   location = {Amsterdam}
 311 }
 312
 313 @ARTICLE{MDP,
 314   author = {Zito Tiziano and Wilbert Niko and Wiskott Laurenz and Berkes Pietro},
 315   title = {Modular toolkit for {D}ata {P}rocessing ({MDP}): a {P}ython data
 316         processing framework},
 317   journal = {Frontiers in Neuroinformatics},
 318   year = {2008},
 319   volume = {2},
 320   abstract = {Modular toolkit for Data Processing (MDP) is a data processing framework
 321         written in Python. From the user&#39;s perspective, MDP is a collection
 322         of supervised and unsupervised learning algorithms and other data
 323         processing units that can be combined into data processing sequences
 324         and more complex feed-forward network architectures. Computations
 325         are performed efficiently in terms of speed and memory requirements.
 326         From the scientific developer&#39;s perspective, MDP is a modular
 327         framework, which can easily be expanded. The implementation of new
 328         algorithms is easy and intuitive. The new implemented units are then
 329         automatically integrated with the rest of the library. MDP has been
 330         written in the context of theoretical research in neuroscience, but
 331         it has been designed to be helpful in any context where trainable
 332         data processing algorithms are used. Its simplicity on the user&#39;s
 333         side, the variety of readily available algorithms, and the reusability
 334         of the implemented units make it also a useful educational tool.},
 335   doi = {10.3389/neuro.11/008.2008},
 336   issn = {ISSN 1662-5196},
 337   owner = {hellboy},
 338   timestamp = {2010.03.05}
 339 }
 340
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 343 @comment{jabref-meta: selector_author:}
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 345 @comment{jabref-meta: selector_journal:}
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