Fast bounded online gradient descent algorithms for scalable kernel-based online learning
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, w...
Saved in:
Main Authors: | ZHAO, Peilin, WANG, Jialei, WU, Pengcheng, JIN, Rong, HOI, Steven C. H. |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2012
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/2342 https://ink.library.smu.edu.sg/context/sis_research/article/3342/viewcontent/Fast_Bounded_Online_Gradient_Descent_Algorithms_for_Scalable_Kernel_Based_Online_Learning.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
An adaptive gradient method for online AUC maximization
by: DING, Yi, et al.
Published: (2015) -
Cost-sensitive online classification
by: WANG, Jialei, et al.
Published: (2014) -
Simple and optimal stochastic gradient methods for nonsmooth nonconvex optimization
by: LI, Zhize, et al.
Published: (2022) -
An integrated constrained gradient descent (iCGD) protocol to correct scan-positional errors for electron ptychography with high accuracy and precision
by: Ning, S, et al.
Published: (2023) -
Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems
by: Eyoh, Imo, et al.
Published: (2020)