- Aylward, S., St. Clair, D.C., Bond, W.E., Flachsbart, B.B., and Rigler, A.K., One-Dimensional Search Strategies for Conjugate Gradient Training of Backpropagation Neural Networks, Intelligent Engineering Systems Through Artificial Neural Networks, Eds. C. Dagli, S. Kumara, and Y. Shin, American Society of Mechanical Engineers Press, November 1992, pp. 197-202.Google Scholar
- Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J., C!assifleation and Regression Trees, Wadsworth & Brooks/Cole, 1984.Google Scholar
- Gallion, R., St. Clair, D.C., Sabharwal, C., and Bond, W.~., Dynamic Id3: A Symbolic Learning Algorithm For Many- Valued Attribute Domains, ACM/SIGAPP Symposium on Applied Computing, Indianapolis, IN, February 1993. Google ScholarDigital Library
- Peterson, G.E. and Ladage, R.N., On Using Sensitivity Analysis to Prune the Inputs to a Neural Network, Intelligefft Engineering Systems Through Artificial Neural Networks, Eds. C. Dagli, $. Kumara, and Y. Shin, American Society of Mechanical Engineers Press, November 1992, pp. 313-318.Google Scholar
- Maher, P.E. and St. Clair, D.C., Uncertain Reasoning in an {1)3 Machine Learning Framework, Submitted to: Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, 1993.Google Scholar
- Quinlan, J.R., Induction of Decision Trees, Machine Learning, Vol. 1, 1986, pp. 81-106. Google ScholarDigital Library
- St. Clair, D.C., Bond, W.E., Rigler, A.K., and Aylwarck S., An Evaluation of Learning Performance in Backpropagation Neural Networks and Decision. Tree Classifier Systems,, 1992 Symposium on Applied Mathematics, Kansas City, MO, March 1992. Google ScholarDigital Library
- St. Clair, D. C., S abharwal, C. L., Hacke, K., and Bond, W. ~., Using Decision-Tree Classifier Systems to Extract Knowledge from Databases, Proceedings of the Fifth Conference on Artificial Intelligence for Space Applications, May 1990. -Google Scholar
- St. Clair, D. C., Bond, W. E., Flachsbart, B. B., and Vigland, A. R., An Architecture for Adaptive Learning in Rule. Based Diagnostic Expert Systems, AI in Armament Workshop - Diagnostics, American Institute of Aeronautics & Astronautics, March 1988.Google Scholar
- Utgoff, P. E., Incremental Induction of Decision Trees, Machine Learning, Vol. 4, No. 2, 1989, pp. 81-106. Google ScholarDigital Library
- VanHorn, B. M., Design Of Backpropagatton Neural Network Architectures Using A Decision Tree Classifier, M.S, Thesis, University of Missouri - Rolla, Rolla, MO, 1992.Google Scholar
Index Terms
- Using the ID3 symbolic classification algorithm to reduce data density
Recommendations
Fuzzy fast classification algorithm with hybrid of ID3 and SVM
Recent Advances in Soft Computing: Theories and ApplicationsThe Classification of data is usually very large database that is the reason we want to classify the large data into different fragmentation of its same type. Already many algorithms have been used for classification like Id3, rule based algorithm, ...
A Combined Classification Algorithm Based on C4.5 and NB
ISICA '08: Proceedings of the 3rd International Symposium on Advances in Computation and IntelligenceWhen our learning task is to build a model with accurate classification, C4.5 and NB are two very important algorithms for achieving this task because of their simplicity and high performance. In this paper, we present a combined classification ...
Improved classification with allocation method and multiple classifiers
We propose a new allocation method for building a classification ensemble.Allocation method uses multiple classifiers: the allocator and micro classifiers.Allocator separates the dataset and allocates them to one of micro classifiers.Allocator is based ...
Comments