Communications of the ACM
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
What size net gives valid generalization?
Advances in neural information processing systems 1
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Vapnik-Chervonenkis dimension of recurent neural networks
Discrete Applied Mathematics - Special issue: Vapnik-Chervonenkis dimension
Almost linear VC-dimension bounds for piecewise polynomial networks
Neural Computation
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
PAC Learning with Generalized Samples and an Applicaiton to Stochastic Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Closest Fit Approach to Missing Attribute VAlues in Preterm Birth Data
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Rough set based approach for inducing decision trees
Knowledge-Based Systems
Ensemble Rough Hypercuboid Approach for Classifying Cancers
IEEE Transactions on Knowledge and Data Engineering
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Probably approximately correct (PAC) learnability of classification models is crucial in machine learning. Many classification algorithms were introduced and simply validated on benchmark data. And they were not further discussed on under what condition they are assured to be learned successfully, because it is commonly hard to address such PAC learning issues. As one may accept, it would be even crucial to investigate the PAC learnability of the classification models if they are exploited to deal with some special data, such as gene microarray data. Rough hypercuboid classifier (RHC) is a novel classifier introduced for classification based on gene microarray data. After analyzing the VC-dimension and the time complexity of RHC, this paper proved that RHC is a PAC-learning model. The proof gives support to the further RHC applications in classifying cancers based on gene microarray data.