IEEE Style Citation: N.V. Saiteja Reddy , T. Srikanth, “Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias),” International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.65-70, 2015.
MLA Style Citation: N.V. Saiteja Reddy , T. Srikanth "Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias)." International Journal of Computer Sciences and Engineering 3.7 (2015): 65-70.
APA Style Citation: N.V. Saiteja Reddy , T. Srikanth, (2015). Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias). International Journal of Computer Sciences and Engineering, 3(7), 65-70.
BibTex Style Citation:
@article{Reddy_2015,
author = {N.V. Saiteja Reddy , T. Srikanth},
title = {Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias)},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2015},
volume = {3},
Issue = {7},
month = {7},
year = {2015},
issn = {2347-2693},
pages = {65-70},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=576},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=576
TI - Class Label Prediction using Back Propagation Algorithm: A comparative study with and without Thresholds (Bias)
T2 - International Journal of Computer Sciences and Engineering
AU - N.V. Saiteja Reddy , T. Srikanth
PY - 2015
DA - 2015/07/30
PB - IJCSE, Indore, INDIA
SP - 65-70
IS - 7
VL - 3
SN - 2347-2693
ER -
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Abstract
The Back propagation Algorithm is a multilayered, feed forward neural network and is one of the most popular and efficient techniques used. This can be used for dataset classification with suitable combination of training, learning and transfer functions. However, there are some problems associated with this Algorithm like Step-size Problem and Local Minima. In this paper we will discuss about the working of the algorithm and efficient ways to perform learning by overcoming the problems in it. We use three common classification problems to illustrate the ways of efficient learning. All the methods and algorithms were implemented using the features of Java.
Key-Words / Index Term
Back Propagation Algorithm, Neural Network, Programming Neural Networks
References
[1] Breast Cancer Wisconsin (Original) Dataset - https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
[2] Iris Data Set:
https://archive.ics.uci.edu/ml/datasets/Iris
[3] J. T. Lalis, B. D. Gerardo and Y. Byun (2014). “An Adaptive Stopping Criterion for Backpropagation Learning in Feedforward Neural Network”. International Journal of Multimedia and Ubiquitous Engineering Vol.9, No. 8, pp. 149-156
[4] JeffHeaton (2005). “Programming Neural Networks in Java”. Heaton Research
[5] Saurabh Karsoliya (2012). “Approximating Number of Hidden layer neurons in MultipleHidden Layer BPNN Architecture”. International Journal of Engineering Trends and Technology-Volume3 Issue6
[6] Tom M. Mitchell (1997). “Machine Learning”. McGraw Hill
[7] Wouter F. Schmidt, Martin A. Kraaijveld and Robert P.W. Duin (1992). “Feed Forward Neural Networks With Random Weights”- Proceedings. 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems
[8] Wine Data Set - https://archive.ics.uci.edu/ml/datasets/Wine
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