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» Learning Functions from Imperfect Positive Data
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EMMCVPR
2011
Springer
12 years 7 months ago
Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study
The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. However, these annotations may be imperfect, in the sense t...
Julian John McAuley, Arnau Ramisa, Tibério ...
NPL
2006
172views more  NPL 2006»
13 years 7 months ago
Adapting RBF Neural Networks to Multi-Instance Learning
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training...
Min-Ling Zhang, Zhi-Hua Zhou
ICCV
2009
IEEE
1019views Computer Vision» more  ICCV 2009»
15 years 15 days ago
Similarity Functions for Categorization: from Monolithic to Category Specific
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjuncti...
Boris Babenko, Steve Branson, Serge Belongie
CVPR
2005
IEEE
14 years 9 months ago
Learning a Similarity Metric Discriminatively, with Application to Face Verification
We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large an...
Sumit Chopra, Raia Hadsell, Yann LeCun
CSB
2005
IEEE
125views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Discriminative Discovery of Transcription Factor Binding Sites from Location Data
Motivation: The availability of genome-wide location analyses based on chromatin immunoprecipitation (ChIP) data gives a new insight for in silico analysis of transcriptional regu...
Yuji Kawada, Yasubumi Sakakibara