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» Learning to Identify Unexpected Instances in the Test Set
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ECCV
2006
Springer
14 years 9 months ago
Learning to Detect Objects of Many Classes Using Binary Classifiers
Viola and Jones [VJ] demonstrate that cascade classification methods can successfully detect objects belonging to a single class, such as faces. Detecting and identifying objects t...
Ramana Isukapalli, Ahmed M. Elgammal, Russell Grei...
SDL
2007
152views Hardware» more  SDL 2007»
13 years 8 months ago
TTCN-3 Quality Engineering: Using Learning Techniques to Evaluate Metric Sets
Software metrics are an essential means to assess software quality. For the assessment of software quality, typically sets of complementing metrics are used since individual metric...
Edith Werner, Jens Grabowski, Helmut Neukirchen, N...
BMCBI
2007
125views more  BMCBI 2007»
13 years 7 months ago
Statistical tests to compare motif count exceptionalities
Background: Finding over- or under-represented motifs in biological sequences is now a common task in genomics. Thanks to p-value calculation for motif counts, exceptional motifs ...
Stéphane Robin, Sophie Schbath, Vincent Van...
CSB
2005
IEEE
129views Bioinformatics» more  CSB 2005»
14 years 29 days ago
Minimal Marker Sets to Discriminate Among Seedlines
Raising seeds for biological experiments is prone to error; a careful experimenter will test in the lab to verify that plants are of the intended strain. Choosing a minimal set of...
Thomas C. Hudson, Ann E. Stapleton, Amy M. Curley
ICML
2006
IEEE
14 years 8 months ago
MISSL: multiple-instance semi-supervised learning
There has been much work on applying multiple-instance (MI) learning to contentbased image retrieval (CBIR) where the goal is to rank all images in a known repository using a smal...
Rouhollah Rahmani, Sally A. Goldman