Communications of the ACM
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
An introduction to computational learning theory
An introduction to computational learning theory
Discrete Applied Mathematics - Special issue: Vapnik-Chervonenkis dimension
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
The Journal of Machine Learning Research
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Applying data mining to software maintenance records
CASCON '03 Proceedings of the 2003 conference of the Centre for Advanced Studies on Collaborative research
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Using support vector machines in data mining
ISTASC'04 Proceedings of the 4th WSEAS International Conference on Systems Theory and Scientific Computation
The Journal of Machine Learning Research
Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A new maximal-margin spherical-structured multi-class support vector machine
Applied Intelligence
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
CHIRP: a new classifier based on composite hypercubes on iterated random projections
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Margin-sparsity trade-off for the set covering machine
ECML'05 Proceedings of the 16th European conference on Machine Learning
Unlabeled compression schemes for maximum classes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Pattern classification via single spheres
DS'05 Proceedings of the 8th international conference on Discovery Science
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
| Hi-index | 0.00 |
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a trade-off between accuracy and complexity. The result is a general-purpose learning machine, suitable for practical learning tasks, that we call the set covering machine. We present a version of the set covering machine that uses data-dependent balls for its set of features and compare its performance with the support vector machine. By extending a technique pioneered by Littlestone and Warmuth, we bound its generalization error as a function of the amount of data compression it achieves during training. In experiments with real-world learning tasks, the bound is shown to be extremely tight and to provide an effective guide for model selection.