Information Processing Letters
The nature of statistical learning theory
The nature of statistical learning theory
Parsimonious Least Norm Approximation
Computational Optimization and Applications
Machine Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Searching with style: authorship attribution in classic literature
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
L0-constrained regression for data mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Author attribution of Turkish texts by feature mining
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Cuisine: Classification using stylistic feature sets and-or name-based feature sets
Journal of the American Society for Information Science and Technology
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
On the utility of incremental feature selection for the classification of textual data streams
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Effective and scalable authorship attribution using function words
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Using relative entropy for authorship attribution
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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In this paper, we use a method proposed by Bradley and Mangasarian "Feature Selection via Concave Minimization and Support Vector Machines" to solve the well-known disputed Federalist Papers classification problem. We find a separating plane that classifies correctly all the "training set" papers of known authorship, based on the relative frequencies of only three words. Using the obtained separating hyperplane in three dimensions, all of the 12 disputed papers ended up on the Madison side of the separating plane. This result coincides with previous work on this problem using other classification techniques.