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» Predicting Time Series with Support Vector Machines
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ICPR
2010
IEEE
15 years 3 months ago
Spatiotemporal-Boosted DCT Features for Head and Face Gesture Analysis
Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial la...
Hatice Çinar Akakin, Bülent Sankur
141
Voted
ICMLA
2010
15 years 1 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...
153
Voted
BMCBI
2008
141views more  BMCBI 2008»
15 years 3 months ago
MiRTif: a support vector machine-based microRNA target interaction filter
Background: MicroRNAs (miRNAs) are a set of small non-coding RNAs serving as important negative gene regulators. In animals, miRNAs turn down protein translation by binding to the...
Yuchen Yang, Yu-Ping Wang, Kuo-Bin Li
147
Voted
IJCNN
2006
IEEE
15 years 9 months ago
Pattern Selection for Support Vector Regression based on Sparseness and Variability
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...
Jiyoung Sun, Sungzoon Cho
174
Voted
ESANN
2006
15 years 4 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...