tex/gostyle.bib

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   4 @INPROCEEDINGS{GellySilver2008,
   5   author = {Gelly, Sylvain and Silver, David},
   6   title = {Achieving master level play in 9x9 computer go},
   7   booktitle = {AAAI'08: Proceedings of the 23rd national conference on Artificial
   8         intelligence},
   9   year = {2008},
  10   pages = {1537--1540},
  11   publisher = {AAAI Press},
  12   abstract = {The UCT algorithm uses Monte-Carlo simulation to estimate the value
  13         of states in a search tree from the current state. However, the first
  14         time a state is encountered, UCT has no knowledge, and is unable
  15         to generalise from previous experience. We describe two extensions
  16         that address these weaknesses. Our first algorithm, heuristic UCT,
  17         incorporates prior knowledge in the form of a value function. The
  18         value function can be learned offline, using a linear combination
  19         of a million binary features, with weights trained by temporal-difference
  20         learning. Our second algorithm, UCT-RAVE, forms a rapid online generalisation
  21         based on the value of moves. We applied our algorithms to the domain
  22         of 9 × 9 Computer Go, using the program MoGo. Using both heuristic
  23         UCT and RAVE, MoGo became the first program to achieve human master
  24         level in competitive play.},
  25   isbn = {978-1-57735-368-3},
  26   location = {Chicago, Illinois}
  27 }
  28
  29 @BOOK{Jolliffe1986,
  30   title = {Principal Component Analysis},
  31   publisher = {Springer, New York},
  32   year = {1986},
  33   author = {I.T. Jolliffe},
  34   owner = {hellboy}
  35 }
  36
  37 @ARTICLE{MDP,
  38   author = {Zito Tiziano and Wilbert Niko and Wiskott Laurenz and Berkes Pietro},
  39   title = {Modular toolkit for Data Processing (MDP): a Python data processing
  40         framework},
  41   journal = {Frontiers in Neuroinformatics},
  42   year = {2008},
  43   volume = {2},
  44   abstract = {Modular toolkit for Data Processing (MDP) is a data processing framework
  45         written in Python. From the user&#39;s perspective, MDP is a collection
  46         of supervised and unsupervised learning algorithms and other data
  47         processing units that can be combined into data processing sequences
  48         and more complex feed-forward network architectures. Computations
  49         are performed efficiently in terms of speed and memory requirements.
  50         From the scientific developer&#39;s perspective, MDP is a modular
  51         framework, which can easily be expanded. The implementation of new
  52         algorithms is easy and intuitive. The new implemented units are then
  53         automatically integrated with the rest of the library. MDP has been
  54         written in the context of theoretical research in neuroscience, but
  55         it has been designed to be helpful in any context where trainable
  56         data processing algorithms are used. Its simplicity on the user&#39;s
  57         side, the variety of readily available algorithms, and the reusability
  58         of the implemented units make it also a useful educational tool.},
  59   doi = {10.3389/neuro.11/008.2008},
  60   issn = {ISSN 1662-5196},
  61   owner = {hellboy},
  62   timestamp = {2010.03.05}
  63 }
  64
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