Communications of the ACM
Computational limitations of small-depth circuits
Computational limitations of small-depth circuits
Learning decision trees from random examples needed for learning
Information and Computation
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
Learning DNF under the uniform distribution in quasi-polynomial time
COLT '90 Proceedings of the third annual workshop on Computational learning theory
The expressive power of voting polynomials
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Learning monotone ku DNF formulas on product distributions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Rank-r decision trees are a subclass of r-decision lists
Information Processing Letters
On learning visual concepts and DNF formulae
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On using the Fourier transform to learn Disjoint DNF
Information Processing Letters
On learning monotone DNF formulae under uniform distributions
Information and Computation
How fast can a threshold gate learn?
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Classification by polynomial surfaces
Discrete Applied Mathematics
Fast learning of k-term DNF formulas with queries
Journal of Computer and System Sciences - Special issue on selected papers presented at the 24th annual ACM symposium on the theory of computing (STOC '92)
Boosting a weak learning algorithm by majority
Information and Computation
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
A subexponential exact learning algorithm for DNF using equivalence queries
Information Processing Letters
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Identification criteria and lower bounds for perceptron-like learning rules
Neural Computation
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
A lower bound for perceptrons and an Oracle separation of the PPPH hierarchy
Journal of Computer and System Sciences
Worst-case analysis of the perceptron and exponentiated update algorithms
Artificial Intelligence
On PAC learning using Winnow, Perceptron, and a Perceptron-like algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Machine Learning
Machine Learning
Learning DNF by Approximating Inclusion-Exclusion Formulae
COCO '99 Proceedings of the Fourteenth Annual IEEE Conference on Computational Complexity
Learning noisy perceptrons by a perceptron in polynomial time
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
A polynomial-time algorithm for learning noisy linear threshold functions
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Toward Attribute Efficient Learning of Decision Lists and Parities
The Journal of Machine Learning Research
Discrete Applied Mathematics
Learning unions of ω(1)-dimensional rectangles
Theoretical Computer Science
Hardness of approximate two-level logic minimization and PAC learning with membership queries
Journal of Computer and System Sciences
Complexity Lower Bounds using Linear Algebra
Foundations and Trends® in Theoretical Computer Science
Pseudorandom generators for polynomial threshold functions
Proceedings of the forty-second ACM symposium on Theory of computing
Optimal bounds for sign-representing the intersection of two halfspaces by polynomials
Proceedings of the forty-second ACM symposium on Theory of computing
Solving linear constraints over real and rational fields
Cybernetics and Systems Analysis
SIAM Journal on Computing
Any AND-OR Formula of Size $N$ Can Be Evaluated in Time $N^{1/2+o(1)}$ on a Quantum Computer
SIAM Journal on Computing
Learning unions of ω(1)-dimensional rectangles
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Private data release via learning thresholds
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Learning DNF by statistical and proper distance queries under the uniform distribution
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Exact learning composed classes with a small number of mistakes
COLT'06 Proceedings of the 19th annual conference on Learning Theory
On PAC learning algorithms for rich boolean function classes
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Making polynomials robust to noise
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
SIAM Journal on Computing
Lower bound on weights of large degree threshold functions
CiE'12 Proceedings of the 8th Turing Centenary conference on Computability in Europe: how the world computes
A PRG for lipschitz functions of polynomials with applications to sparsest cut
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Dual lower bounds for approximate degree and markov-bernstein inequalities
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
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Using techniques from learning theory, we show that any s-term DNF over n variables can be computed by a polynomial threshold function of degree O(n1/3 log s). This upper bound matches, up to a logarithmic factor, the longstanding lower bound given by Minsky and Papert in their 1968 book Perceptrons. As a consequence of this upper bound we obtain the fastest known algorithm for learning polynomial size DNF, one of the central problems in computational learning theory.