Supervised Learning in the Gene Ontology Part II: A Bottom-Up Algorithm

2005 ◽  
pp. 98-124 ◽  
Author(s):  
Herman Midelfart
Keyword(s):  
Gene Ontology ◽  
Supervised Learning ◽  
Bottom Up

scholarly journals GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Kévin Rue-Albrecht ◽  
Paul A. McGettigan ◽  
Belinda Hernández ◽  
Nicolas C. Nalpas ◽  
David A. Magee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Supervised Learning ◽  
Gene Expression Data ◽  
Expression Data ◽  
Bioconductor Package

A Novel Bottom-Up Semi-Supervised Learning Framework for Salient Object Detection

2021 ◽  
Author(s):  
Yu Pang ◽  
Hao Wu ◽  
Yunhe Wu ◽  
Chengdong Wu ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Object Detection ◽  
Supervised Learning ◽  
Salient Object Detection ◽  
Salient Object ◽  
Bottom Up ◽  
Learning Framework

scholarly journals Applicability of semi-supervised learning assumptions for gene ontology terms prediction

2016 ◽  
Author(s):  
Jorge Alberto Jaramillo-Garzón ◽  
◽  
César Germán Castellanos-Domínguez ◽  
Alexandre Perera-Lluna ◽  
◽  
...  
Keyword(s):  
Gene Ontology ◽  
Supervised Learning

scholarly journals A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification

2019 ◽  
Vol 33 ◽  
pp. 8738-8745 ◽  
Author(s):  
Yutian Lin ◽  
Xuanyi Dong ◽  
Liang Zheng ◽  
Yan Yan ◽  
Yi Yang
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Real World Data ◽  
Bottom Up ◽  
Data Volume ◽  
Clustering Approach ◽  
The Individual ◽  
The Relationship

Most person re-identification (re-ID) approaches are based on supervised learning, which requires intensive manual annotation for training data. However, it is not only resourceintensive to acquire identity annotation but also impractical to label the large-scale real-world data. To relieve this problem, we propose a bottom-up clustering (BUC) approach to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples. Our algorithm considers two fundamental facts in the re-ID task, i.e., diversity across different identities and similarity within the same identity. Specifically, our algorithm starts with regarding individual sample as a different identity, which maximizes the diversity over each identity. Then it gradually groups similar samples into one identity, which increases the similarity within each identity. We utilizes a diversity regularization term in the bottom-up clustering procedure to balance the data volume of each cluster. Finally, the model achieves an effective trade-off between the diversity and similarity. We conduct extensive experiments on the large-scale image and video re-ID datasets, including Market-1501, DukeMTMCreID, MARS and DukeMTMC-VideoReID. The experimental results demonstrate that our algorithm is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably than competing transfer learning and semi-supervised learning methods.

Aggregating Welfare Indicators into an Operational Welfare Assessment System: A Bottom-up Approach

2001 ◽  
Vol 51 (2) ◽  
pp. 53-57 ◽  
Author(s):  
Tine Rousing ◽  
Marianne Bonde ◽  
Jan Tind Sørensen
Keyword(s):  
Assessment System ◽  
Bottom Up ◽  
Welfare Assessment ◽  
Welfare Indicators ◽  
System A

Psychology and Culture: Top Down Versus Bottom Up?

PsycCRITIQUES ◽  
2005 ◽  
Vol 50 (19) ◽  
Author(s):  
Michael Cole
Keyword(s):  
Top Down ◽  
Bottom Up

Top-down vs. bottom-up: When instructions overcome automatic retrieval

2011 ◽  
Author(s):  
A. Kiesel ◽  
F. Waszak ◽  
R. Pfister
Keyword(s):  
Top Down ◽  
Bottom Up ◽  
Automatic Retrieval
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