A new polynomial-time algorithm for linear programming
Combinatorica
The Capacity of Multilevel Threshold Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational limitations on learning from examples
Journal of the ACM (JACM)
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?
Neural Computation
Results on learnability and the Vapnik-Chervonenkis dimension
Information and Computation
Computational learning theory: an introduction
Computational learning theory: an introduction
Learning with discrete multivalued neurons
Journal of Computer and System Sciences
On specifying Boolean functions by labelled examples
Discrete Applied Mathematics
The nature of statistical learning theory
The nature of statistical learning theory
Linear decision lists and partitioning algorithms for the construction of neural networks
FoCM '97 Selected papers of a conference on Foundations of computational mathematics
Multiple threshold neural logic
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Discrete mathematics of neural networks: selected topics
Discrete mathematics of neural networks: selected topics
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Dualization, decision lists and identification of monotone discrete functions
Annals of Mathematics and Artificial Intelligence
On Learning Sets and Functions
Machine Learning
Machine Learning
On the Computational Power of Boolean Decision Lists
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Minimization of Multivalued Multithreshold Perceptrons using Genetic Algorithms
ISMVL '98 Proceedings of the The 28th International Symposium on Multiple-Valued Logic
Learning with Permutably Homogenous Multiple-Valued Multiple-Threshold Perceptrons
ISMVL '98 Proceedings of the The 28th International Symposium on Multiple-Valued Logic
ISMVL '99 Proceedings of the Twenty Ninth IEEE International Symposium on Multiple-Valued Logic
Generalization Error Bounds for Threshold Decision Lists
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Accurately learning from few examples with a polyhedral classifier
Computational Optimization and Applications
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We analyze theoretically the generalization properties of multi-class data classification techniques that are based on iteratively partitioning the data points by hyperplanes. A special case is that in which the data points of different classes are separated by a number of parallel hyperplanes, and we investigate the algorithmics of finding a suitable partitioning in this case.