天平秤数据集，用于模拟心理实验结果
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University of California, Irvine
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*数据结构实际以真实数据为准
Data Set Information:
这个数据集是用来模拟心理实验结果的。每个示例被分类为天平尖端向右、尖端向左或处于平衡状态。这些属性是左权重、左距离、右权重和右距离。找到类的正确方法是（左距离*左权重）和（右距离*右权重）中的较大值。如果它们相等，则是平衡的。
Attribute Information:
1.课程名称：3（L、B、R）
2.左侧重量：5（1,2,3,4,5）
3.左距离：5（1，2，3，4，5）
4.右权重：5（1,2,3,4,5）
5.右距离：5（1，2，3，4，5）
Relevant Papers:
Klahr, D., & Siegler, R.S. (1978). The Representation of Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in Child Development and Behavior, pp. 61-116. New York: Academic Press
[Web link]
Langley,P. (1987). A General Theory of Discrimination Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production System Models of Learning and Development, pp. 99-161. Cambridge, MA: MIT Press
[Web link]
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press
[Web link]
McClelland, J.L. (1988). Parallel Distibuted Processing: Implications for Cognition and Development. Technical Report AIP-47, Department of Psychology, Carnegie-Mellon University
[Web link]
Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling Cognitive Development on Balance Scale Phenomena. Machine Learning, Vol. 16, pp. 59-88.
[Web link]
Papers That Cite This Data Set1:
Jianbin Tan and David L. Dowe. MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes. Australian Conference on Artificial Intelligence. 2003. [View Context].
Zhi-Hua Zhou and Yuan Jiang and Shifu Chen. Extracting symbolic rules from trained neural network ensembles. AI Commun, 16. 2003. [View Context].
Peter Sykacek and Stephen J. Roberts. Adaptive Classification by Variational Kalman Filtering. NIPS. 2002. [View Context].
Remco R. Bouckaert. Accuracy bounds for ensembles under 0 { 1 loss. Xtal Mountain Information Technology & Computer Science Department, University of Waikato. 2002. [View Context].
Nir Friedman and Moisés Goldszmidt and Thomas J. Lee. Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. ICML. 1998. [View Context].
Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
Hirotaka Inoue and Hiroyuki Narihisa. Experiments with an Ensemble Self-Generating Neural Network. Okayama University of Science. [View Context].
Alexander K. Seewald. meta-Learning for Stacked Classification. Austrian Research Institute for Artificial Intelligence. [View Context].
Generated to model psychological experiments reported by Siegler, R. S. (1976).
Three Aspects of Cognitive Development. Cognitive Psychology, 8, 481-520.
Donor:
Tim Hume (hume '@' ics.uci.edu)
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