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» Requirements for Machine Lifelong Learning
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130
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FGR
2011
IEEE
255views Biometrics» more  FGR 2011»
14 years 7 months ago
Beyond simple features: A large-scale feature search approach to unconstrained face recognition
— Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) that transform raw pixel va...
David D. Cox, Nicolas Pinto
135
Voted
IADIS
2004
15 years 5 months ago
Detecting Unauthorized Use in Online Journal Archives: A Case Study
JSTOR is a not-for-profit online library containing a full back-run of digitized versions of a large number of academic journals. In order to help defray costs for maintaining the...
Paul Seligman, Sean W. Smith
SIGMETRICS
2008
ACM
109views Hardware» more  SIGMETRICS 2008»
15 years 3 months ago
Automatic request categorization in internet services
Modeling system performance and workload characteristics has become essential for efficiently provisioning Internet services and for accurately predicting future resource requirem...
Abhishek Sharma, Ranjita Bhagwan, Monojit Choudhur...
136
Voted
ICDCS
2002
IEEE
15 years 8 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
118
Voted
COLT
1999
Springer
15 years 7 months ago
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estim...
Avrim Blum, Adam Kalai, John Langford