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Hellerstein, Lisa - Department of Computer Science and Engineering, Polytechnic Institute of New York University
R u t c o r R e p o r t
On the Power of Finite Automata with both Nondeterministic and Probabilistic States
Functions that are ReadOnce on a Subset of their Inputs Lisa Hellerstein*
Learning DNF Formulas: A Lisa Hellerstein
Independence and Port Oracles for Matroids, with an Application to Computational Learning
On PAC learning algorithms for rich Boolean function classes Lisa Hellerstein #
Conjunctions of Unate DNF Formulas: Learning and Structure Aaron Feigelson Lisa Hellerstein \Lambda
This is a preprint. The nal version of this paper appeared in Journal of Computer and System Sciences, 70(4):435{470, Exact learning of DNF formulas using DNF
Learning in the Presence of Finitely or Infinitely Many Irrelevant Avrim Blum \Lambda
How Many Queries are Needed to Learn? Lisa Hellerstein, y EECS Dept., Northwestern University
Attributeefficient learning in query and mistakebound models Nader Bshouty \Lambda
Exact learning of DNF formulas using DNF hypotheses [Extended Abstract]
On the Generation of 2Dimensional Index Workloads Joseph M. Hellerstein, U.C. Berkeley \Lambda
Computational Complexity vol. 4 (1994), 37--61. AN ALGORITHM TO LEARN
Algorithms for Distributional and Adversarial Pipelined Filter Ordering Problems
Learning ReadOnce Formulas with Queries Dana Angluin \Lambda Lisa Hellerstein y Marek Karpinski z
The Forbidden Projections of Unate Functions Aaron Feigelson Lisa Hellerstein \Lambda
On Compression-Based Text Classification Yuval Marton1
Parallel Pipelined Filter Ordering with Precedence Constraints
Why Skewing Works: Learning Di#cult Boolean Functions with Greedy Tree Learners
Learning Boolean ReadOnce Formulas over Generalized Bases
On Compression-Based Text Classification Yuval Marton1