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Coding techniques for handling failures in large disk arrays
TLDR
We address the problem of designing erasure-correcting binary linear codes that protect against the loss of data caused by disk failures in large disk arrays. Expand
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  • PDF
How many queries are needed to learn?
TLDR
We investigate the query complexity of exact learning in the membership and (proper) equivalence query model. Expand
  • 89
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On Compression-Based Text Classification
TLDR
Compression-based text classification methods are easy to apply, requiring virtually no preprocessing of the data. Expand
  • 77
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Learning read-once formulas with queries
TLDR
A read-once formula is a Boolean formula in which each variable occurs, at most, once. Expand
  • 217
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Failure correction techniques for large disk arrays
TLDR
The ever increasing need for I/O bandwidth will be met with ever larger arrays of disks. Expand
  • 53
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On generalized constraints and certificates
TLDR
We show that a set of Boolean functions can be characterized by a finite set of generalized constraints iff the set is closed under the operations of permutation of variables and addition of dummy variables. Expand
  • 26
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Learning Boolean Read-Once Formulas over Generalized Bases
TLDR
A formula is read-once if each variable appears on at most a single input. Expand
  • 37
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How Many Queries Are Needed to Learn?
TLDR
We investigate the query complexity of exact learning in the membership and (proper) equivalence query model. Expand
  • 33
  • 4
Learning read-once formulas over fields and extended bases
TLDR
A formula is read-once if each variable in it occurs at most once. Expand
  • 42
  • 4
Learning in the presence of finitely or infinitely many irrelevant attributes
TLDR
This paper addresses the problem of learning boolean functions in query and mistake-bound models in the presence of irrelevant attributes. Expand
  • 41
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