The Use of Multiclass Support Vector Machines and Probabilistic Neural Networks for Signal Classification and Noise Detection in PLC/OFDM Channels

2020 ◽  
Author(s):  
Dalal H. Baroud ◽  
Ali N. Hasan ◽  
T. Shongwe
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Signal Classification ◽  
Support Vector ◽  
Noise Detection ◽  
Probabilistic Neural Networks ◽  
Vector Machines ◽  
Multiclass Support 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.

ON THE INFLUENCE OF PARAMETER θ- ON PERFORMANCE OF RBF NEURAL NETWORKS TRAINED WITH THE DYNAMIC DECAY ADJUSTMENT ALGORITHM

2006 ◽  
Vol 16 (04) ◽  
pp. 271-281 ◽  
Author(s):  
ADRIANO L. I. OLIVEIRA ◽  
ERICLES A. MEDEIROS ◽  
THYAGO A. B. V. ROCHA ◽  
MIGUEL E. R. BEZERRA ◽  
RONALDO C. VERAS
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Classification Performance ◽  
Support Vector ◽  
Rbf Neural Networks ◽  
Probabilistic Neural Networks ◽  
Generalization Performance ◽  
Vector Machines ◽  
Two Parameters ◽  
Dynamic Decay Adjustment

The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks (RBFNs) and probabilistic neural networks (PNNs). The algorithm has two parameters, namely, θ+ and θ-. The papers which introduced DDA argued that those parameters would not heavily influence classification performance and therefore they recommended using always the default values of these parameters. In contrast, this paper shows that smaller values of parameter θ- can, for a considerable number of datasets, result in strong improvement in generalization performance. The experiments described here were carried out using twenty benchmark classification datasets from both Proben1 and the UCI repositories. The results show that for eleven of the datasets, the parameter θ- strongly influenced classification performance. The influence of θ- was also noticeable, although much less, on six of the datasets considered. This paper also compares the performance of RBF-DDA with θ- selection with both AdaBoost and Support Vector Machines (SVMs).

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