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CVPR
2005
IEEE
14 years 7 months ago
Robust Boosting for Learning from Few Examples
We present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of our original data. The motivation is that since the learning algori...
Lior Wolf, Ian Martin
FLAIRS
2008
13 years 7 months ago
Selecting Minority Examples from Misclassified Data for Over-Sampling
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
Jorge de la Calleja, Olac Fuentes, Jesús Go...
CVPR
2000
IEEE
14 years 7 months ago
Learning from One Example through Shared Densities on Transforms
We define a process called congealing in which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based ...
Erik G. Miller, Nicholas E. Matsakis, Paul A. Viol...
ICCV
2009
IEEE
14 years 10 months ago
Detection of Human Actions From A Single Example
We present an algorithm for detecting human actions based upon a single given video example of such actions. The proposed method is unsupervised, does not require learning, segm...
Hae Jong Seo, Peyman Milanfar
MM
2005
ACM
172views Multimedia» more  MM 2005»
13 years 11 months ago
Learning the semantics of multimedia queries and concepts from a small number of examples
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for seman...
Apostol Natsev, Milind R. Naphade, Jelena Tesic