69

Extracting Comprehensible Models from Trained Neural Networks
– W. Craven
 1996


Lower Bounds in . . . Learning Theory via Analytic Methods
– Alexander Alexandrovich Sherstov
 2009


Computational Learning Theory
– Sally A. Goldman

26

Learning Functions Represented as Multiplicity Automata
– Amos Beimel, Francesco Bergadano, Nader H. Bshouty, Eyal Kushilevitz, Stefano Varricchio
 2000

47

Applying the Weak Learning Framework to Understand and Improve C4.5
– Tom Dietterich, Michael Kearns, Yishay Mansour
 1996

30

Learning DNF Over The Uniform Distribution Using A Quantum Example Oracle
– Nader H. Bshouty, Jeffrey C. Jackson
 1995

422

Selection of relevant features and examples in machine learning
– Avrim L. Blum, Pat Langley
 1997

2

Faithful Representations and Moments of Satisfaction: Probabilistic Methods in Learning and Logic
– Lidror Troyansky, Prof Naftali Tishby
 1998

110

An introduction to boosting and leveraging
– Ron Meir, Gunnar Rätsch
 2003

28

On Using Extended Statistical Queries to Avoid Membership Queries
– Nader H. Bshouty, Vitaly Feldman, Dana Ron
 2002

2

Decision Trees: More Theoretical Justification For Practical Algorithms
– Amos Fiat, Dmitry Pechyony

162

An Efficient MembershipQuery Algorithm for Learning DNF with Respect to the Uniform Distribution
– Jeffrey C. Jackson
 1994

6

Distributionspecific agnostic boosting
– Vitaly Feldman
 2010

38

Boosting and hardcore sets
– Adam R. Klivans, Rocco A. Servedio
 1999

514

An Efficient Boosting Algorithm for Combining Preferences
– Raj Dharmarajan Iyer , Jr.
 1999

564

A Short Introduction to Boosting
– Yoav Freund, Robert E. Schapire
 1999

13

Learning with Queries Corrupted by Classification Noise
– Jeffrey Jackson, Eli Shamir, Clara Shwartzman
 1999


Learning DNF over the Uniform Distribution . . .
– Nader H. Bshouty, Jeffrey Jackson
 1994

17

Learning DNF from Random Walks
– Nader Bshouty, et al.
 2003
