Communications of the ACM
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Cryptographic primitives based on hard learning problems
CRYPTO '93 Proceedings of the 13th annual international cryptology conference on Advances in cryptology
Learning in the presence of finitely or infinitely many irrelevant attributes
Journal of Computer and System Sciences
Adaptive versus nonadaptive attribute-efficient learning
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Machine Learning
Machine Learning
Optimal Attribute-Efficient Learning of Disjunction, Parity and Threshold Functions
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
PAC learning with irrelevant attributes
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Improved Bounds for Testing Juntas
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Testing juntas: a brief survey
Property testing
Testing juntas: a brief survey
Property testing
On attribute efficient and non-adaptive learning of parities and DNF expressions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
| Hi-index | 0.00 |
A new research frontier in AI and data mining seeks to develop methods to automatically discover relevant variables among many irrelevant ones. In this paper, we present four algorithms that output such crucial variables in PAC model with membership queries. The first algorithm executes the task under any unknown distribution by measuring the distance between virtual and real targets. The second algorithm exhausts virtual version space under an arbitrary distribution. The third algorithm exhausts universal set under the uniform distribution. The fourth algorithm measures influence of variables under the uniform distribution. Knowing the number r of relevant variables, the first algorithm runs in almost linear time for r. The second and the third ones use less membership queries than the first one, but runs in time exponential for r. The fourth one enumerates highly influential variables in quadratic time for r.