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ACL
1994
13 years 10 months ago
A Markov Language Learning Model for Finite Parameter Spaces
This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explici...
Partha Niyogi, Robert C. Berwick
DM
2006
128views more  DM 2006»
13 years 9 months ago
Non-trivial t-designs without repeated blocks exist for all t
A computer package is being developed at Bayreuth for the generation and investigation of discrete structures. The package is a C and C++ class library of powerful algorithms endow...
Luc Teirlinck
WWW
2006
ACM
14 years 9 months ago
Compressing and searching XML data via two zips
XML is fast becoming the standard format to store, exchange and publish over the web, and is getting embedded in applications. Two challenges in handling XML are its size (the XML...
Paolo Ferragina, Fabrizio Luccio, Giovanni Manzini...
JACM
2006
99views more  JACM 2006»
13 years 9 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
KDD
2007
ACM
153views Data Mining» more  KDD 2007»
14 years 9 months ago
Exploiting duality in summarization with deterministic guarantees
Summarization is an important task in data mining. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic q...
Panagiotis Karras, Dimitris Sacharidis, Nikos Mamo...