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» Support Vector Classification with Input Data Uncertainty
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NIPS
1994
13 years 9 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
ICPR
2008
IEEE
14 years 2 months ago
Spam filtering with several novel bayesian classifiers
In this paper, we report our work on spam filtering with three novel bayesian classification methods: Aggregating One-Dependence Estimators (AODE), Hidden Naïve Bayes (HNB), Loca...
Chuanliang Chen, Yingjie Tian, Chunhua Zhang
ICMLA
2010
13 years 5 months ago
Using Randomised Vectors in Transcription Factor Binding Site Predictions
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may ...
Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alis...
ICMCS
2005
IEEE
148views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Facial Expression Recognition with Relevance Vector Machines
For many decades automatic facial expression recognition has scientifically been considered a real challenging problem in the fields of pattern recognition or robotic vision. The ...
Dragos Datcu, Léon J. M. Rothkrantz
AAAI
2008
13 years 10 months ago
Prediction and Change Detection in Sequential Data for Interactive Applications
We consider the problems of sequential prediction and change detection that arise often in interactive applications: A semi-automatic predictor is applied to a time-series and is ...
Jun Zhou, Li Cheng, Walter F. Bischof