.png){kind=icon}
返回
Generalized SMO Algorithm for SVM-Based Multitask Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2012.2187307.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as learning with structured data and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+ MTL for classification. Training the SVM+ MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+ MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+ MTL setting. Empirical results show that, for typical SVM+ MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.
Keyword:
Classification
learning with structured data
multitask learning
quadratic optimization
sequential minimal optimization
support vector machine (SVM)
SVM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.5K
被引数:
7.2W
机构
暂无机构信息
引用论文
Local and Systemic Effects of Immune Checkpoint Blockade on Relapsed Myeloid Malignancies Following Allogeneic Hematopoietic Stem Cell Transplantation.png)
Blood
OAAI
BloodIF0
OAAI分享收藏Clinical significance of PD-L1 and PD-L2 copy number gains in non-small-cell lung cancerOncotarget
OAAI
OncotargetIF0
OAAI分享收藏 没有更多内容
没有更多内容