Improving Training Speed of Support Vector Machines by Creating Exploitable Trends of Lagrangian Variables: An Application to DNA Splice Site Detection

2007 ◽  
Author(s):  
Jason Li ◽  
Saman K. Halgamuge
Keyword(s):  
Support Vector Machines ◽  
Splice Site ◽  
Support Vector ◽  
Lagrangian Variables ◽  
Vector Machines

scholarly journals Accurate splice site prediction using support vector machines

2007 ◽  
Vol 8 (Suppl 10) ◽  
pp. S7 ◽  
Author(s):  
Sören Sonnenburg ◽  
Gabriele Schweikert ◽  
Petra Philips ◽  
Jonas Behr ◽  
Gunnar Rätsch
Keyword(s):  
Support Vector Machines ◽  
Splice Site ◽  
Support Vector ◽  
Splice Site Prediction ◽  
Site Prediction ◽  
Vector Machines

scholarly journals Accuracy test in identifying the splice site type of DNA sequences by using probabilistic neural networks and support vector machines

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Djati Kerami
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Splice Site ◽  
Dna Sequences ◽  
Support Vector ◽  
Probabilistic Neural Networks ◽  
Application Problem ◽  
Vector Machines ◽  
Accuracy Level

It has been known that Probabilistic Neural Networks as machine learning is very fast in it’s computation time and give a better accuracy comparing to another type of neural networks, on solving a real-world application problem. In the recent years, Support Vector Machines has become a popular model over other machine learning. It can be analyzed theoretically and can achieve a good performance at same time. This paper will describe the use of those machines learning to solve pattern recognition problems with a preliminary case study in detecting the type of splice site on the DNA sequences, particularity on the accuracy level. The results obtained show that Support Vector Machines have a good accuracy level about 95 % comparing to Probabilistic Neural Networks with 92 % approximately.

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