239 search results - page 14 / 48 » Semi-supervised Learning from Unbalanced Labeled Data - An I... |
ML
2006
ACM
13 years 7 months ago
2006
ACM
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall i...
CVPR
2009
IEEE
15 years 2 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
2008
13 years 9 months ago
2008
We apply robust Bayesian decision theory to improve both generative and discriminative learners under bias in class proportions in labeled training data, when the true class propo...
ICCV
2009
IEEE
15 years 19 days ago
2009
IEEE
Labeling image collections is a tedious task, especially
when multiple labels have to be chosen for each image. In
this paper we introduce a new framework that extends state
of ...
MM
2006
ACM
14 years 1 months ago
2006
ACM
Labeling objects in images is an essential prerequisite for many visual learning and recognition applications that depend on training data, such as image retrieval, object detecti...