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Showing 2014 · cs.LGShow all
3 papers · 2 filters
cs.LG2014
Conic Multi-Task Classification
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Averag…
cs.LG2014★ 31 cited
Pareto-Path Multi-Task Multiple Kernel Learning
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) share…
cs.LG2014★ 13 cited
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data
Liyue Zhao, Yu Zhang, Gita Sukthankar
Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers. Unfortunately, poor worker performanc…