Description

Please ask Gail Gong for further information on this database.

Related Papers

  • Christophe Giraud and Tony Martinez. ADYNAMIC INCREMENTAL NETWORK THAT LEARNS BY DISCRIMINATION. AA. [link]
  • Wl/odzisl/aw Duch and Karol Grudzinski. Ensembles of Similarity-based Models. Intelligent Information Systems. 2001. [link]
  • Jinyan Li and Limsoon Wong. Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL. WAIM. 2003. [link]
  • Wl/odzisl/aw Duch and Karol Grudzinski and Geerd H. F Diercksen. Minimal distance neural methods. Department of Computer Methods, Nicholas Copernicus University. [link]
  • Elena Smirnova and Ida G. Sprinkhuizen-Kuyper and I. Nalbantis and b. ERIM and Universiteit Rotterdam. Unanimous Voting using Support Vector Machines. IKAT, Universiteit Maastricht. [link]
  • Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. An Ant Colony Based System for Data Mining: Applications to Medical Data. CEFET-PR, CPGEI Av. Sete de Setembro, 3165. [link]
  • 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. [link]
  • Wl/odzisl/aw Duch and Rafal Adamczak and Geerd H. F Diercksen. Classification, Association and Pattern Completion using Neural Similarity Based Methods. Department of Computer Methods, Nicholas Copernicus University. [link]
  • Chris Drummond and Robert C. Holte. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. Institute for Information Technology, National Research Council Canada. [link]
  • Floriana Esposito and Donato Malerba and Giovanni Semeraro. A Comparative Analysis of Methods for Pruning Decision Trees. IEEE Trans. Pattern Anal. Mach. Intell, 19. 1997. [link]
  • Amaury Habrard and Marc Bernard and Marc Sebban. IOS Press Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data. Fundamenta Informaticae. 2004. [link]
  • Gary M. Weiss and Haym Hirsh. A Quantitative Study of Small Disjuncts: Experiments and Results. Department of Computer Science Rutgers University. 2000. [link]
  • Wl/odzisl/aw Duch and Rafal/ Adamczak Email:duchraad@phys. uni. torun. pl. Statistical methods for construction of neural networks. Department of Computer Methods, Nicholas Copernicus University. [link]
  • Federico Divina and Elena Marchiori. Handling Continuous Attributes in an Evolutionary Inductive Learner. Department of Computer Science Vrije Universiteit. [link]
  • Peter D. Turney. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm. CoRR, csAI/9503102. 1995. [link]
  • Zhi-Hua Zhou and Xu-Ying Liu. Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. [link]
  • Zhi-Hua Zhou and Yuan Jiang and Shifu Chen. Extracting symbolic rules from trained neural network ensembles. AI Commun, 16. 2003. [link]
  • Takao Mohri and Hidehiko Tanaka. An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes. Information Engineering Course, Faculty of Engineering The University of Tokyo. [link]
  • Wl odzisl and Rafal Adamczak and Krzysztof Grabczewski. Optimization of Logical Rules Derived by Neural Procedures. Department of Computer Methods, Nicholas Copernicus University. [link]
  • Suresh K. Choubey and Jitender S. Deogun and Vijay V. Raghavan and Hayri Sever. A comparison of feature selection algorithms in the context of rough classifiers. [link]
  • Wl/odzisl/aw Duch and Rafal Adamczak and Geerd H. F Diercksen. Neural Networks from Similarity Based Perspective. Department of Computer Methods, Nicholas Copernicus University. [link]
  • Michael L. Raymer and Travis E. Doom and Leslie A. Kuhn and William F. Punch. Knowledge discovery in medical and biological datasets using a hybrid Bayes classifier/evolutionary algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 33. 2003. [link]
  • David W. Opitz and Richard Maclin. Popular Ensemble Methods: An Empirical Study. J. Artif. Intell. Res. (JAIR, 11. 1999. [link]
  • Christophe Giraud and Tony Martinez and Christophe G. Giraud-Carrier. University of Bristol Department of Computer Science ILA: Combining Inductive Learning with Prior Knowledge and Reasoning. 1995. [link]
  • Ida G. Sprinkhuizen-Kuyper and Elena Smirnova and I. Nalbantis. Reliability yields Information Gain. IKAT, Universiteit Maastricht. [link]
  • Rafael S. Parpinelli and Heitor S. Lopes and Alex Alves Freitas. PART FOUR: ANT COLONY OPTIMIZATION AND IMMUNE SYSTEMS Chapter X An Ant Colony Algorithm for Classification Rule Discovery. CEFET-PR, Curitiba. [link]
  • Petri Kontkanen and Petri Myllym and Tomi Silander and Henry Tirri and Peter Gr. On predictive distributions and Bayesian networks. Department of Computer Science, Stanford University. 2000. [link]
  • Ron Kohavi. The Power of Decision Tables. ECML. 1995. [link]
  • Takashi Matsuda and Hiroshi Motoda and Tetsuya Yoshida and Takashi Washio. Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. Discovery Science. 2002. [link]
  • Yk Huhtala and Juha Krkkinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998. [link]
  • Gabor Melli. A Lazy Model-Based Approach to On-Line Classification. University of British Columbia. 1989. [link]
  • Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997. [link]
  • Xiaoli Z. Fern and Carla Brodley. Boosting Lazy Decision Trees. ICML. 2003. [link]
  • [link]
  • [link]

Related datasets