tex/gostyle.bib

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   3
   4 @MISC{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 @ARTICLE{CoverHart1967,
  13   author = {Thomas M. Cover and Peter E. Hart},
  14   title = {Nearest neighbor pattern classification},
  15   journal = {IEEE Transactions on Information Theory},
  16   year = {1967},
  17   volume = {13},
  18   pages = {21-27},
  19   number = {1},
  20   owner = {hellboy},
  21   timestamp = {2010.03.10}
  22 }
  23
  24 @INPROCEEDINGS{GellySilver2008,
  25   author = {Gelly, Sylvain and Silver, David},
  26   title = {Achieving master level play in 9x9 computer go},
  27   booktitle = {AAAI'08: Proceedings of the 23rd national conference on Artificial
  28         intelligence},
  29   year = {2008},
  30   pages = {1537--1540},
  31   publisher = {AAAI Press},
  32   abstract = {The UCT algorithm uses Monte-Carlo simulation to estimate the value
  33         of states in a search tree from the current state. However, the first
  34         time a state is encountered, UCT has no knowledge, and is unable
  35         to generalise from previous experience. We describe two extensions
  36         that address these weaknesses. Our first algorithm, heuristic UCT,
  37         incorporates prior knowledge in the form of a value function. The
  38         value function can be learned offline, using a linear combination
  39         of a million binary features, with weights trained by temporal-difference
  40         learning. Our second algorithm, UCT-RAVE, forms a rapid online generalisation
  41         based on the value of moves. We applied our algorithms to the domain
  42         of 9 × 9 Computer Go, using the program MoGo. Using both heuristic
  43         UCT and RAVE, MoGo became the first program to achieve human master
  44         level in competitive play.},
  45   isbn = {978-1-57735-368-3},
  46   location = {Chicago, Illinois}
  47 }
  48
  49 @BOOK{Jolliffe1986,
  50   title = {Principal Component Analysis},
  51   publisher = {Springer, New York},
  52   year = {1986},
  53   author = {I.T. Jolliffe},
  54   owner = {hellboy}
  55 }
  56
  57 @ELECTRONIC{KohonenPy,
  58   author = {lmjohns3},
  59   title = {python-kohonen, {A} library of {Kohonen} maps},
  60   howpublished = {Released under {MIT} License},
  61   url = {http://code.google.com/p/python-kohonen/},
  62   owner = {hellboy},
  63   timestamp = {2010.03.10}
  64 }
  65
  66 @TECHREPORT{Nissen2003,
  67   author = {S. Nissen},
  68   title = {Implementation of a Fast Artificial Neural Network Library (fann)},
  69   institution = {Department of Computer Science University of Copenhagen (DIKU)},
  70   year = {2003},
  71   note = {http://fann.sf.net},
  72   owner = {hellboy},
  73   timestamp = {2010.03.10}
  74 }
  75
  76 @ARTICLE{Pearson,
  77   author = {J. L. Rodgers and W. A. Nicewander},
  78   title = {{Thirteen ways to look at the correlation coefficient}},
  79   journal = {The American Statistician},
  80   year = {1988},
  81   month = {Feb},
  82   volume = {42},
  83   number = {1},
  84   pages = {59--66},
  85   issn = {},
  86   owner = {pasky}
  87 }
  88
  89 @ELECTRONIC{Python25,
  90   author = {{Python Software Foundation}},
  91   month = {September},
  92   year = {2006},
  93   title = {Python 2.5},
  94   url = {http://www.python.org/dev/peps/pep-0356/},
  95   owner = {hellboy},
  96   timestamp = {2009.04.29}
  97 }
  98
  99 @INPROCEEDINGS{Riedmiller1993,
 100   author = {Martin Riedmiller and Heinrich Braun},
 101   title = {{A Direct Adaptive Method for Faster Backpropagation Learning: The
 102         RPROP Algorithm}},
 103   booktitle = {IEEE International Conference on Neural Networks},
 104   year = {1993},
 105   pages = {586--591},
 106   owner = {hellboy},
 107   timestamp = {2010.03.07}
 108 }
 109
 110 @ARTICLE{MDP,
 111   author = {Zito Tiziano and Wilbert Niko and Wiskott Laurenz and Berkes Pietro},
 112   title = {Modular toolkit for {D}ata {P}rocessing ({MDP}): a {P}ython data
 113         processing framework},
 114   journal = {Frontiers in Neuroinformatics},
 115   year = {2008},
 116   volume = {2},
 117   abstract = {Modular toolkit for Data Processing (MDP) is a data processing framework
 118         written in Python. From the user&#39;s perspective, MDP is a collection
 119         of supervised and unsupervised learning algorithms and other data
 120         processing units that can be combined into data processing sequences
 121         and more complex feed-forward network architectures. Computations
 122         are performed efficiently in terms of speed and memory requirements.
 123         From the scientific developer&#39;s perspective, MDP is a modular
 124         framework, which can easily be expanded. The implementation of new
 125         algorithms is easy and intuitive. The new implemented units are then
 126         automatically integrated with the rest of the library. MDP has been
 127         written in the context of theoretical research in neuroscience, but
 128         it has been designed to be helpful in any context where trainable
 129         data processing algorithms are used. Its simplicity on the user&#39;s
 130         side, the variety of readily available algorithms, and the reusability
 131         of the implemented units make it also a useful educational tool.},
 132   doi = {10.3389/neuro.11/008.2008},
 133   issn = {ISSN 1662-5196},
 134   owner = {hellboy},
 135   timestamp = {2010.03.05}
 136 }
 137
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