Communications of the ACM
The Capacity of Multilevel Threshold Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring decision trees using the minimum description length principle
Information and Computation
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
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
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A sharp concentration inequality with application
Random Structures & Algorithms
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
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
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Machine Learning
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Localized Rademacher Complexities
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A few notes on statistical learning theory
Advanced lectures on machine learning
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Function Learning from Interpolation
Combinatorics, Probability and Computing
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
On data classification by iterative linear partitioning
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
On the generalization error of fixed combinations of classifiers
Journal of Computer and System Sciences
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Improving SVM classifiers training using artificial samples
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
On data classification by iterative linear partitioning
Discrete Applied Mathematics
Automatic threshold estimation for data matching applications
Information Sciences: an International Journal
Generalization error bounds for the logical analysis of data
Discrete Applied Mathematics
Hierarchical linear support vector machine
Pattern Recognition
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
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In this paper we consider the generalization accuracy of classification methods based on the iterative use of linear classifiers. The resulting classifiers, which we call threshold decision lists act as follows. Some points of the data set to be classified are given a particular classification according to a linear threshold function (or hyperplane). These are then removed from consideration, and the procedure is iterated until all points are classified. Geometrically, we can imagine that at each stage, points of the same classification are successively chopped off from the data set by a hyperplane. We analyse theoretically the generalization properties of data classification techniques that are based on the use of threshold decision lists and on the special subclass of multilevel threshold functions. We present bounds on the generalization error in a standard probabilistic learning framework. The primary focus in this paper is on obtaining generalization error bounds that depend on the levels of separation---or margins---achieved by the successive linear classifiers. We also improve and extend previously published theoretical bounds on the generalization ability of perceptron decision trees.