Learning decision trees from random examples needed for learning
Information and Computation
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Learning boolean functions in an infinite attribute space
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
SIAM Journal on Computing
Rank-r decision trees are a subclass of r-decision lists
Information Processing Letters
An introduction to computational learning theory
An introduction to computational learning theory
Learning in the presence of finitely or infinitely many irrelevant attributes
Journal of Computer and System Sciences
Perceptrons, PP, and the polynomial hierarchy
Computational Complexity - Special issue on circuit complexity
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Artificial Intelligence - Special issue on relevance
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Identification of partial disjunction, parity, and threshold functions
Theoretical Computer Science
Computing Boolean functions by polynomials and threshold circuits
Computational Complexity
Computational sample complexity and attribute-efficient learning
Journal of Computer and System Sciences
Machine Learning
Perceptron, Winnow, and PAC Learning
SIAM Journal on Computing
Machine Learning
Machine Learning
Machine Learning
On the Computational Power of Boolean Decision Lists
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
New degree bounds for polynomial threshold functions
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
On online learning of decision lists
The Journal of Machine Learning Research
Journal of Computer and System Sciences - STOC 2001
Learning intersections and thresholds of halfspaces
Journal of Computer and System Sciences - Special issue on FOCS 2002
SIAM Journal on Computing
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We consider two well-studied problems regarding attribute efficient learning: learning decision lists and learning parity functions. First, we give an algorithm for learning decision lists of length k over n variables using 2Õ(k1/3) log n examples and time nÕ(k1/3). This is the first algorithm for learning decision lists that has both subexponential sample complexity and subexponential running time in the relevant parameters. Our approach is based on a new construction of low degree, low weight polynomial threshold functions for decision lists. For a wide range of parameters our construction matches a lower bound due to Beigel for decision lists and gives an essentially optimal tradeoff between polynomial threshold function degree and weight. Second, we give an algorithm for learning an unknown parity function on k out of n variables using O(n1-1/k) examples in poly(n) time. For k=o(log n) this yields the first polynomial time algorithm for learning parity on a superconstant number of variables with sublinear sample complexity. We also give a simple algorithm for learning an unknown length-k parity using O(k log n) examples in nk/2 time, which improves on the naive nk time bound of exhaustive search.