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ECML
2007
Springer
15 years 8 months ago
Learning to Classify Documents with Only a Small Positive Training Set
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
Xiaoli Li, Bing Liu, See-Kiong Ng
79
Voted
ESA
2010
Springer
220views Algorithms» more  ESA 2010»
15 years 2 months ago
Data Structures for Storing Small Sets in the Bitprobe Model
abstract appeared in Proc. 24th Annual Symposium of Computational Geometry (SoCG), 2008, pp. 338-345. S. Shannigrahi and S. P. Pal, "Efficient Prufer-like Coding and Counting ...
Jaikumar Radhakrishnan, Smit Shah 0001, Saswata Sh...
122
Voted
ISCA
1993
IEEE
112views Hardware» more  ISCA 1993»
15 years 6 months ago
Working Sets, Cache Sizes, and Node Granularity Issues for Large-Scale Multiprocessors
The distribution of resources among processors, memory and caches is a crucial question faced by designers of large-scale parallel machines. If a machine is to solve problems with...
Edward Rothberg, Jaswinder Pal Singh, Anoop Gupta
99
Voted
ICALP
2005
Springer
15 years 7 months ago
Noisy Turing Machines
Abstract. Turing machines exposed to a small stochastic noise are considered. An exact characterisation of their (≈ Π0 2 ) computational power (as noise level tends to 0) is obt...
Eugene Asarin, Pieter Collins
COLING
1990
15 years 3 months ago
Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries
In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrate the use of...
Jean Véronis, Nancy Ide