.png){kind=icon}
返回
Multi-view based unlabeled data selection using feature transformation methods for semiboost learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.04.021.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
SemiBoost Mallapragada et al. (2009) is a boosting framework for semi-supervised learning, in which unlabeled data as well as labeled data both contribute to learning. Various strategies have been proposed in the literature to perform the task of selecting useful unlabeled data in SemiBoost. Recently, a multi-view based strategy was proposed in Le and Kim (2016), in which the feature set of the data is decomposed into subsets (i.e., multiple views) using a feature-decomposition method. In the decomposition process, the strategy inevitably results in some loss of information. To avoid this drawback, this paper considered feature-transformation methods, rather than using the decomposition method, to obtain the multiple views. More specifically, in the feature-transformation method, a number of views were obtained from the entire feature set using the same number of different mapping functions. After deriving the number of views of the data, each of the views was used for measuring corresponding confidences, for first evaluating examples to be selected. Then, all the confidence levels measured from the multiple views were combined as a weighted average for deriving a target confidence. The experimental results, which were obtained using support vector machines for well-known benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of the traditional strategies. In addition, the results demonstrate that when the data is transformed appropriately into multiple views, the strategy can achieve further improvement in results in terms of classification accuracy. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
SemiBoost learning
Useful unlabeled data selection
Multiple views of feature set
Feature decomposition methods
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Adaptive Impedance Control to Enhance Human Skill on a Haptic Interface System自适应阻抗控制以增强触觉接口系统上的人类技能
OAAI
OAAI分享收藏Ionic Associations and Hydration in the Electrical Double Layer of Water-in-Salt Electrolytes盐中水电解质的双电层中的离子缔合和水合作用
OAAI
OAAI分享收藏CYP2B6‐G516T genotype influences plasma efavirenz concentration in a Hong Kong population, allowing potential individualization of therapy.png)
HIV MedicineOAAI
HIV MedicineIF0
OAAI分享收藏On measuring confidence levels using multiple views of feature set for useful unlabeled data selectionNEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容