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