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Using Low-Rank Approximations to Speed Up Kernel Logistic Regression Algorithm

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Affiliation of Author(s):Chongqing University of Posts & Telecommunications

Journal:IEEE ACCESS

Place of Publication:IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA

Key Words:Kernel logistic regression; sequential minimal optimization; dual problem; low-rank approximation

Indexed by:Journal paper

Document Code:000475931500001

Discipline:Engineering

First-Level Discipline:电子与信息类

Document Type:J

Volume:7

Issue:1

Page Number:84242-84252

ISSN No.:2169-3536

Translation or Not:no

Date of Publication:2019-08-01

Included Journals:SCI

Date of Publication:2019-08-01

Next One:Maximal Uncorrelated Multinomial Logistic Regression

Lei Dajiang

Gender:Male Education Level:Postgraduate (Doctoral) Alma Mater:Chongqing University Degree:Doctoral Degree in Engineering Status:在岗 School/Department:College of Computer Business Address:IT Building 2010 Contact Information:leidj@cqupt.edu.cn E-Mail:

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Using Low-Rank Approximations to Speed Up Kernel Logistic Regression Algorithm