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» A New Perspective on Collection Selection
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CVPR
2009
IEEE
15 years 3 months ago
An Instance Selection Approach to Multiple Instance Learning
Multiple-instance Learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from whi...
Zhouyu Fu (Australian National University), Antoni...
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 2 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi
JMLR
2010
165views more  JMLR 2010»
13 years 3 months ago
Feature Selection: An Ever Evolving Frontier in Data Mining
The rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities in production, services, comm...
Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao
ADBIS
1999
Springer
104views Database» more  ADBIS 1999»
14 years 23 days ago
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classifica...
Alexey Tsymbal, Seppo Puuronen, Vagan Y. Terziyan
ECOOP
2008
Springer
13 years 10 months ago
Online Phase-Adaptive Data Layout Selection
Good data layouts improve cache and TLB performance of object-oriented software, but unfortunately, selecting an optimal data layout a priori is NP-hard. This paper introduces layo...
Chengliang Zhang, Martin Hirzel