A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms

Author(s):  
R. Udaiyakumar ◽  
N. Vijayalakshmi ◽  
M. Prashanthram ◽  
S. Jayaprakash
2013 ◽  
Vol 4 (3) ◽  
pp. 354-360 ◽  
Author(s):  
Dr. Rafiqul Zaman Khan ◽  
Haider Allamy

Supervised machine learning is an important task for learning artificial neural networks; therefore a demand for selected supervised learning algorithms such as back propagation algorithm, decision tree learning algorithm and perceptron algorithm has been arise in order to perform the learning stage of the artificial neural networks. In this paper; a comparative study has been presented for the aforementioned algorithms to evaluate their performance within a range of specific parameters such as speed of learning, overfitting avoidance, and their accuracy. Besides these parameters we have included their benefits and limitations to unveil their hidden features and provide more details regarding their performance. We have found the decision tree algorithm is the best as compared with other algorithms that can solve the complex problems with a remarkable speed.


2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana

2019 ◽  
Vol 6 (4) ◽  
pp. 12
Author(s):  
ABUBAKAR UMAR ◽  
A. BASHIR SULAIMON ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
S. ADEBAYO OLAWALE ◽  
◽  
...  

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