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IEE
2002
72views more  IEE 2002»
13 years 6 months ago
Making inferences with small numbers of training sets
This paper discusses a potential methodological problem with empirical studies assessing project effort prediction systems. Frequently a hold-out strategy is deployed so that the ...
Colin Kirsopp, Martin J. Shepperd
SMI
2008
IEEE
154views Image Analysis» more  SMI 2008»
14 years 1 months ago
SHREC'08 entry: Training set expansion via autotags
Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external...
Corey Goldfeder, Haoyun Feng, Peter K. Allen
ECML
2007
Springer
14 years 27 days ago
Learning to Classify Documents with Only a Small Positive Training Set
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
Xiaoli Li, Bing Liu, See-Kiong Ng
IJCNN
2000
IEEE
13 years 11 months ago
Fog Forecasting Using Self Growing Neural Network 'CombNET-II: ' A Solution for Imbalanced Training Sets Problem
This paper proposes a method to solve problem that comes with imbalanced training sets which is often seen in the practical applications. We modi ed Self Growing Neural Network Co...
Anto Satriyo Nugroho, Susumu Kuroyanagi, Akira Iwa...
BMCBI
2005
152views more  BMCBI 2005»
13 years 6 months ago
Ranking the whole MEDLINE database according to a large training set using text indexing
Background: The MEDLINE database contains over 12 million references to scientific literature, ut 3/4 of recent articles including an abstract of the publication. Retrieval of ent...
Brian P. Suomela, Miguel A. Andrade