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» Feature Extraction for Massive Data Mining
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PR
2011
14 years 7 months ago
A survey of multilinear subspace learning for tensor data
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
WSDM
2010
ACM
215views Data Mining» more  WSDM 2010»
16 years 1 months ago
Boilerplate Detection using Shallow Text Features
In addition to the actual content Web pages consist of navigational elements, templates, and advertisements. This boilerplate text typically is not related to the main content, ma...
Christian Kohlschütter, Peter Fankhauser, Wol...
RAID
2009
Springer
15 years 11 months ago
PE-Miner: Mining Structural Information to Detect Malicious Executables in Realtime
In this paper, we present an accurate and realtime PE-Miner framework that automatically extracts distinguishing features from portable executables (PE) to detect zero-day (i.e. pr...
M. Zubair Shafiq, S. Momina Tabish, Fauzan Mirza, ...
CBMS
2005
IEEE
15 years 6 months ago
Local Dimensionality Reduction within Natural Clusters for Medical Data Analysis
Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before apply...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
15 years 11 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...