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KDD
2012
ACM
207views Data Mining» more  KDD 2012»
13 years 9 months ago
Robust multi-task feature learning
Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning alg...
Pinghua Gong, Jieping Ye, Changshui Zhang
ATAL
2003
Springer
15 years 12 months ago
Customizing AOSE methodologies by reusing AOSE features
Future large-scale software development projects will require engineering support for a diverse range of software quality attributes, such as privacy and openness. It is not feasi...
Thomas Juan, Leon Sterling, Maurizio Martelli, Viv...
JSS
2006
127views more  JSS 2006»
15 years 6 months ago
An approach to feature location in distributed systems
This paper describes an approach to the feature location problem for distributed systems, that is, to the problem of locating which code components are important in providing a pa...
Dennis Edwards, Sharon Simmons, Norman Wilde
CIKM
2010
Springer
15 years 5 months ago
Novel local features with hybrid sampling technique for image retrieval
In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, ...
Leszek Kaliciak, Dawei Song, Nirmalie Wiratunga, J...
ICCV
2011
IEEE
14 years 6 months ago
Informative Feature Selection for Object Recognition via Sparse PCA
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to r...
Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry