PUMA

academical publication management
collect, organize, and share publications

( en | de )

 

Disambiguation of "Fan, Qi"

Author of the publication

copydeleteadd this publication to your clipboard
MPEKDyL: Efficient multi-partial empirical kernel dynamic learning.

, , , and . Knowl.-Based Syst., (2015)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Qi Qi University of Stuttgart

Dr. Sisi Fan University of Stuttgart

Qi Han

Tian Qiu University of Stuttgart

Nicola Fanton University of Stuttgart

 

Other publications of authors with the same name

An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection., , , and . Neural Processing Letters, 42 (3): 715-744 (2015)Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem, , , , , and . CoRR, (2009)VCExplorer: A Interactive Graph Exploration Framework Based on Hub Vertices with Graph Consolidation., , , , , and . CoRR, (2017)A survey on computation offloading in the mobile cloud computing environment., , , and . IJCAT, 59 (2): 106-113 (2019)Towards Neighborhood Window Analytics over Large-Scale Graphs., , , and . DASFAA (2), volume 9643 of Lecture Notes in Computer Science, page 201-217. Springer, (2016)Multiple Empirical Kernel Learning with dynamic pairwise constraints., , and . Appl. Soft Comput., (2015)MREKLM: A fast multiple empirical kernel learning machine., , , and . Pattern Recognition, (2017)Simulation optimization via promising region search and surrogate model approximation., and . Winter Simulation Conference, page 649-658. IEEE, (2016)Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing., , , and . Concurrency and Computation: Practice and Experience, (2017)High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest., , , , and . IEEE Access, (2019)