Cause-effect relationships and partially defined Boolean functions
Annals of Operations Research
The Strength of Weak Learnability
Machine Learning
Learning in the presence of malicious errors
SIAM Journal on Computing
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Journal of Computer and System Sciences
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Logical analysis of numerical data
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear Programming Boosting via Column Generation
Machine Learning
The Maximum Box Problem and its Application to Data Analysis
Computational Optimization and Applications
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Branch-and-Bound Algorithms for the Test Cover Problem
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Finding Essential Attributes from Binary Data
Annals of Mathematics and Artificial Intelligence
Smooth boosting and learning with malicious noise
The Journal of Machine Learning Research
Maximizing agreements and coagnostic learning
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
Optimal Hardness Results for Maximizing Agreements with Monomials
CCC '06 Proceedings of the 21st Annual IEEE Conference on Computational Complexity
Ruling Out PTAS for Graph Min-Bisection, Dense k-Subgraph, and Bipartite Clique
SIAM Journal on Computing
Binarized Support Vector Machines
INFORMS Journal on Computing
Chance constrained uncertain classification via robust optimization
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
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The NP-hard maximum monomial agreement problem consists of finding a single logical conjunction that is most consistent with or “best fits” a weighted data set of “positive” and “negative” binary vectors. Computing weighted voting classifiers using boosting methods involves a maximum agreement subproblem at each iteration, although such subproblems are typically solved in practice by heuristic methods. Here, we describe an exact branch-and-bound method for maximum agreement over Boolean monomials, improving on the earlier work of Goldberg and Shan [Goldberg, N., C. Shan. 2007. Boosting optimal logical patterns. Proc. 7th SIAM Internat. Conf. Data Mining, SIAM, Philadelphia, 228--236]. Specifically, we develop a tighter upper bounding function and an improved branching procedure that exploits knowledge of the bound and the particular data set, while having a lower branching factor. Experimental results show that the new method is able to solve larger problem instances and runs faster within a linear programming boosting procedure applied to medium-sized data sets from the UCI Machine Learning Repository. The new algorithm also runs much faster than applying a commercial mixed-integer programming solver, which uses linear programming relaxation-based bounds, to an integer linear programming formulation of the problem.