58 search results - page 1 / 12 » Learning a Distance Metric from Multi-instance Multi-label D... |
NPL
2006
13 years 4 months ago
2006
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...
CVPR
2012
IEEE
11 years 7 months ago
2012
IEEE
In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization p...
CVPR
2009
IEEE
14 years 11 months ago
2009
IEEE
Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
...
NIPS
2003
13 years 5 months ago
2003
This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach,...
CVPR
2007
IEEE
14 years 6 months ago
2007
IEEE
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...