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» Learning from Highly Structured Data by Decomposition
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SIGIR
2012
ACM
13 years 6 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
BMCBI
2008
220views more  BMCBI 2008»
15 years 4 months ago
Gene prediction in metagenomic fragments: A large scale machine learning approach
Background: Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. ...
Katharina J. Hoff, Maike Tech, Thomas Lingner, Rol...
ICMLA
2007
15 years 5 months ago
Memory-based context-sensitive spelling correction at web scale
We study the problem of correcting spelling mistakes in text using memory-based learning techniques and a very large database of token n-gram occurrences in web text as training d...
Andrew Carlson, Ian Fette
PVLDB
2010
90views more  PVLDB 2010»
15 years 2 months ago
The HV-tree: a Memory Hierarchy Aware Version Index
The huge amount of temporal data generated from many important applications call for a highly efficient and scalable version index. The TSB-tree has the potential of large scalab...
Rui Zhang, Martin Stradling
EOR
2007
165views more  EOR 2007»
15 years 4 months ago
Adaptive credit scoring with kernel learning methods
Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression, linear regression and discriminant analysis are the most popular...
Yingxu Yang