Data Set Information:

The dataset format is described below.  Note: the format of this database was modified on 2/26/90 to conform with the format of all the other databases in the UCI repository of machine learning databases.

Attribute Information:

Classes (2):  -- White-can-win ("won") and White-cannot-win ("nowin").

I believe that White is deemed to be unable to win if the Black pawn can safely advance.

Attributes: see Shapiro's book.

Relevant Papers:

Alen D. Shapiro (1983,1987), "Structured Induction in Expert Systems", Addison-Wesley.  This book is based on Shapiro's Ph.D. thesis (1983) at the University of Edinburgh entitled "The Role of Structured Induction in Expert Systems".
[Web link]

Stephen Muggleton (1987), "Structuring Knowledge by Asking Questions", pp.218-229 in "Progress in Machine Learning", edited by I. Bratko and Nada Lavrac, Sigma Press, Wilmslow, England  SK9 5BB.
[Web link]

Robert C. Holte, Liane Acker, and Bruce W. Porter (1989), "Concept Learning and the Problem of Small Disjuncts", Proceedings of IJCAI.  Also available as technical report AI89-106, Computer Sciences Department, University of Texas at Austin, Austin, Texas 78712.
[Web link]

Papers That Cite This Data Set¹:

Manuel Oliveira. Library Release Form Name of Author: Stanley Robson de Medeiros Oliveira Title of Thesis: Data Transformation For Privacy-Preserving Data Mining Degree: Doctor of Philosophy Year this Degree Granted. University of Alberta Library. 2005.  [View Context].

Marcus Hutter and Marco Zaffalon. Distribution of Mutual Information from Complete and Incomplete Data. CoRR, csLG/0403025. 2004.  [View Context].

Ira Cohen and Fabio Gagliardi Cozman and Nicu Sebe and Marcelo Cesar Cirelo and Thomas S. Huang. Semisupervised Learning of Classifiers: Theory, Algorithms, and Their Application to Human-Computer Interaction. IEEE Trans. Pattern Anal. Mach. Intell, 26. 2004.  [View Context].

Douglas Burdick and Manuel Calimlim and Jason Flannick and Johannes Gehrke and Tomi Yiu. MAFIA: A Performance Study of Mining Maximal Frequent Itemsets. FIMI. 2003.  [View Context].

Russell Greiner and Wei Zhou. Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. AAAI/IAAI. 2002.  [View Context].

Tanzeem Choudhury and James M. Rehg and Vladimir Pavlovic and Alex Pentland. Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection. ICPR (3). 2002.  [View Context].

Marco Zaffalon and Marcus Hutter. Robust Feature Selection by Mutual Information Distributions. CoRR, csAI/0206006. 2002.  [View Context].

Michael G. Madden. evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002.  [View Context].

James Bailey and Thomas Manoukian and Kotagiri Ramamohanarao. Fast Algorithms for Mining Emerging Patterns. PKDD. 2002.  [View Context].

Jie Cheng and Russell Greiner. Learning Bayesian Belief Network Classifiers: Algorithms and System. Canadian Conference on AI. 2001.  [View Context].

Boonserm Kijsirikul and Sukree Sinthupinyo and Kongsak Chongkasemwongse. Approximate Match of Rules Using Backpropagation Neural Networks. Machine Learning, 44. 2001.  [View Context].

Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao and Limsoon Wong. DeEPs: A New Instance-based Discovery and Classification System. Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases. 2001.  [View Context].

Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao. Instance-based Classification by Emerging Patterns. PKDD. 2000.  [View Context].

Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999.  [View Context].

Yk Huhtala and Juha K?rkk?inen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998.  [View Context].

Adam J. Grove and Dale Schuurmans. Boosting in the Limit: Maximizing the Margin of Learned Ensembles. AAAI/IAAI. 1998.  [View Context].

Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996.  [<a href="../support/Chess+(King-Rook+vs.+King-Pawn)

Database originally generated and described by Alen Shapiro.

Donor/Coder:

Rob Holte (holte '@' uottawa.bitnet).  

The database was supplied to Holte by Peter Clark of the Turing Institute in Glasgow (pete '@' turing.ac.uk).