A Feasibility Study of Distance-Based Learning Algorithms That Utilize Labeled and Unlabeled Training Data
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === Supervised learning is one of the important types of learning mechanisms in machine learning. It typically requires sufficient prelabeled training data from which to learn a prediction model that is faithful to the target concept. However, in practice, prepar...
Main Authors: | Chen, Yu-Sing, 陳育興 |
---|---|
Other Authors: | Hu, Yuh-Jyh |
Format: | Others |
Language: | zh-TW |
Published: |
2018
|
Online Access: | http://ndltd.ncl.edu.tw/handle/wmh596 |
Similar Items
-
Pattern recognition using labelled and unlabelled data
by: Petrakieva, Lina
Published: (2004) -
Learning with unlabeled data.
Published: (2009) -
Document Clustering with Labeled and Unlabeled Data Using Constrained-PLSA
by: Chen, Chun-Hsien, et al.
Published: (2011) -
Gene function prediction using labeled and unlabeled data
by: Wang Yong, et al.
Published: (2008-01-01) -
A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data
by: Yuchen Wang, et al.
Published: (2019-01-01)