1 % This file was created with JabRef 2.6b2.
5 author = {Ales Cieply and others
},
6 title = {EGF ratings system
-- System description
},
8 timestamp
= {2011.03.10},
9 url
= {http
://www.europeangodatabase.eu
/EGD
/EGF_rating_system.php
}
13 author = {Ales Cieply and others
},
14 title = {Go Ratings of Professional and Strong Amateur Players
},
16 url
= {http
://www.goweb.cz
/progor
}
19 @ELECTRONIC
{KGSAnalytics
,
21 title = {KGS Analytics
},
23 url
= {http
://kgs.gosquares.net
/}
27 author = {Ulrich G\"ortz
},
28 title = {Kombilo
--- a go database program
},
30 url
= {http
://www.u
-go.net
/kombilo
/}
34 author = {Frank de Groot
},
35 title = {Moyo Go Studio
},
37 url
= {http
://www.moyogo.com
/}
41 author = {Arno Hollosi
},
42 title = {SGF File Format
},
44 url
= {http
://www.red
-bean.com
/sgf
/}
48 author = {Gunnar Farneb\"
{a
}ck
},
49 title = {GTP
--- Go Text Protocol
},
51 url
= {http
://www.lysator.liu.se
/~gunnar
/gtp
/}
54 @ELECTRONIC
{RankComparison
,
55 author = {Sensei's Library
},
56 title = {Rank
--- worldwide comparison
},
58 url
= {http
://senseis.xmp.net
/?RankWorldwideComparison
}
62 author = {Jean
-loup Gailly
, Bill Hosken and others
},
63 title = {The Go Teaching Ladder
},
65 url
= {http
://gtl.xmp.net
/}
69 author = {T. Mark Hall
, John Fairbairn
},
70 title = {Games of Go on Disk
--- GoGoD Encyclopaedia and Database
},
72 url
= {http
://www.gogod.co.uk
/}
75 @ELECTRONIC
{GoDiscThread
,
76 author = {GoDiscussions
},
77 title = {Pro Styles
(discussion thread
)},
79 url
= {http
://www.godiscussions.com
/forum
/showthread.php?t
=10980}
83 author = {Petr Baudi\v
{s
} and others
},
84 title = {Pachi
--- Simple Go
/Baduk
/Weiqi Bot
},
86 url
= {http
://repo.or.cz
/w
/pachi.git
}
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
},
93 url
= {http
://repo.or.cz
/w
/gostyle.git
}
96 @inproceedings
{SpatPat
,
97 author = {Stern
, David and Herbrich
, Ralf and Graepel
, Thore
},
98 title = {Bayesian pattern ranking for move prediction in the game of Go
},
99 booktitle = {ICML '
06: Proceedings of the
23rd international conference on Machine learning
},
101 isbn
= {1-59593-383-2},
103 location
= {Pittsburgh
, Pennsylvania
},
104 doi
= {http
://doi.acm.org
/10.1145/1143844.1143954},
106 address = {New York
, NY
, USA
},
109 @inproceedings
{PatElo
,
110 title = { {C
}omputing
{E
}lo
{R
}atings of
{M
}ove
{P
}atterns in the
{G
}ame of
{G
}o
},
111 author = {{C
}oulom
, {R
}{\'e
}mi
},
112 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.},
113 language
= {{A
}nglais
},
114 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
},
115 booktitle = {{C
}omputer
{G
}ames
{W
}orkshop
},
116 address = {{A
}msterdam
{P
}ays
-{B
}as
},
117 editor = {van den
{H
}erik
, {H
}.
{J
}aap and
{M
}ark
{W
}inands and
{J
}os
{U
}iterwijk and
{M
}aarten
{S
}chadd
},
118 audience
= {non sp
{\'e
}cifi
{\'e
}e
},
120 URL
= {http
://hal.inria.fr
/inria
-00149859/en
/},
121 URL
= {http
://hal.inria.fr
/inria
-00149859/PDF
/MMGoPatterns.pdf
},
123 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
},
125 @ARTICLE
{CoverHart1967
,
126 author = {Thomas M. Cover and Peter E. Hart
},
127 title = {Nearest neighbor pattern classification
},
128 journal = {IEEE Transactions on Information Theory
},
134 timestamp
= {2010.03.10}
137 @INPROCEEDINGS
{GellySilver2008
,
138 author = {Gelly
, Sylvain and Silver
, David
},
139 title = {Achieving master level play in
9x9 computer go
},
140 booktitle = {AAAI'
08: Proceedings of the
23rd national conference on Artificial
143 pages = {1537--1540},
144 publisher = {AAAI Press
},
145 abstract = {The UCT algorithm uses Monte
-Carlo simulation to estimate the value
146 of states in a search tree from the current state. However
, the first
147 time a state is encountered
, UCT has no knowledge
, and is unable
148 to generalise from previous experience. We describe two extensions
149 that
address these weaknesses. Our first algorithm
, heuristic UCT
,
150 incorporates prior knowledge in the form of a value function. The
151 value function can be learned offline
, using a linear combination
152 of a million binary features
, with weights trained by temporal
-difference
153 learning. Our second algorithm
, UCT
-RAVE
, forms a rapid online generalisation
154 based on the value of moves. We applied our algorithms to the domain
155 of
9 ×
9 Computer Go
, using the program MoGo. Using both heuristic
156 UCT and RAVE
, MoGo became the first program to achieve human master
157 level in competitive play.
},
158 isbn
= {978-1-57735-368-3},
159 location
= {Chicago
, Illinois
}
163 title = {Principal Component Analysis
},
164 publisher = {Springer
, New York
},
166 author = {I.T. Jolliffe
},
171 title = {The rating of chessplayers
, past and present
},
172 publisher = {Arco
, New York
},
174 author = {Arpad E. Elo
},
179 @ELECTRONIC
{KohonenPy
,
181 title = {python
-kohonen
, {A
} library of
{Kohonen
} maps
},
182 howpublished = {Released under
{MIT
} License
},
183 url
= {http
://code.google.com
/p
/python
-kohonen
/},
185 timestamp
= {2010.03.10}
188 @TECHREPORT
{Nissen2003
,
189 author = {S. Nissen
},
190 title = {Implementation of a Fast Artificial Neural Network Library
(fann
)},
191 institution = {Department of Computer Science University of Copenhagen
(DIKU
)},
193 note = {http
://fann.sf.net
},
195 timestamp
= {2010.03.10}
199 author = {J. L. Rodgers and W. A. Nicewander
},
200 title = {{Thirteen ways to look at the correlation coefficient
}},
201 journal = {The American Statistician
},
210 @ELECTRONIC
{Python25
,
211 author = {{Python Software Foundation
}},
214 title = {Python
2.5},
215 url
= {http
://www.python.org
/dev
/peps
/pep
-0356/},
217 timestamp
= {2009.04.29}
220 @INPROCEEDINGS
{Riedmiller1993
,
221 author = {Martin Riedmiller and Heinrich Braun
},
222 title = {{A Direct Adaptive Method for Faster Backpropagation Learning
: The
224 booktitle = {IEEE International Conference on Neural Networks
},
228 timestamp
= {2010.03.07}
232 author = {Zito Tiziano and Wilbert Niko and Wiskott Laurenz and Berkes Pietro
},
233 title = {Modular toolkit for
{D
}ata
{P
}rocessing
({MDP
}): a
{P
}ython data
234 processing framework
},
235 journal = {Frontiers in Neuroinformatics
},
238 abstract = {Modular toolkit for Data Processing
(MDP
) is a data processing framework
239 written in Python. From the user
&#
39;s perspective
, MDP is a collection
240 of supervised and unsupervised learning algorithms and other data
241 processing units that can be combined into data processing sequences
242 and more complex feed
-forward network architectures. Computations
243 are performed efficiently in terms of speed and memory requirements.
244 From the scientific developer
&#
39;s perspective
, MDP is a modular
245 framework
, which can easily be expanded. The implementation of new
246 algorithms is easy and intuitive. The new implemented units are then
247 automatically integrated with the rest of the library. MDP has been
248 written in the context of theoretical research in neuroscience
, but
249 it has been designed to be helpful in any context where trainable
250 data processing algorithms are used. Its simplicity on the user
&#
39;s
251 side
, the variety of readily available algorithms
, and the reusability
252 of the implemented units make it also a useful educational tool.
},
253 doi
= {10.3389/neuro
.11/008.2008},
254 issn
= {ISSN
1662-5196},
256 timestamp
= {2010.03.05}
259 @comment
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261 @comment
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263 @comment
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265 @comment
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