Artificial Neural Network Learning: A Comparative Review

Neocleous, Costas; Schizas, Christos N.

doi:10.1007/3-540-46014-4_27

Cited by 34 publications

(19 citation statements)

References 31 publications

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“…Then, all the weights in the net are adjusted slightly in the direction that would bring the output values of the net closer to the values for the desired output. There are several algorithms with which a network can be trained (Neocleous and Schizas 2002 • η is a positive number (called learning rate), which determines the step size in the gradient descent search. A large value enables back propagation to move faster to the target weight configuration but it also increases the chance of its never reaching this target.…”

Section: Neural Networkmentioning

confidence: 99%

Machine learning: a review of classification and combining techniques

2006

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Supervised classification is one of the tasks most frequently carried out by socalled Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy-ensembles of classifiers.

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Section: Neural Networkmentioning

confidence: 99%

Machine learning: a review of classification and combining techniques

2006

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“…More recently, many new training algorithms have been proposed to overcome the drawbacks of traditional neural networks and to increase their reliability (Bianchini and Gori, 1996;Neocleous and Schizas, 2002). In this paradigm, one of the significant developments is a class of kernel based neural networks called Support Vector Machines (SVMs), the principle of which is rooted in the statistical learning theory and method of structural risk minimization (Haykin, 2003).…”

Section: Support Vector Machinementioning

confidence: 99%

Downscaling of precipitation for climate change scenarios: A support vector machine approach

Tripathi

Srinivas

Nanjundiah

2006

Journal of Hydrology

517

316

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“…There are several algorithms by which a network can be trained [17], but the most popular algorithm is back propagation (BP) algorithm. The back propagation algorithm will perform a number of weight modification before it concludes with a good weight configuration for n training instances and w weights each epoch in learning takes O(nw) time.…”

Section: Perceptron Based Techniquesmentioning

confidence: 99%

Prediction of Breast Cancer Biopsy Outcomes an Approach using Machine Leaning Perspectives

Chaurasia¹,

Chakrabarti²,

Chourasia³

2014

IJCA

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Breast cancer is the most frequently diagnosed cancer in USA. Furthermore breast cancer is the second major cause of death for women in USA. Several well established tools are currently used for screening for breast cancer including clinical breast exam, mammograms and ultrasound. Mammography is one of the most effective in terms of accuracy and cost. However the low positive predicted value (PPV) of breast cancer biopsies resulting from mammograms leads to 70% unnecessary biopsies with benign outcomes. In order to reduce the large number of surgical biopsies of breast, several CAD based system has been proposed in the last decades. Using these systems the radiologist gets an aid on their decision to perform breast biopsies. The dataset used is based on BIRADS findings. Prior work achieves good result with decision tree and neural network. The paper use AutoMLP, BP (back propagation) neural network and support vector machine (SVM) approach to predict the outcomes of mammogram with better result. Using SVM the false biopsies should significantly reduced to only 13%.

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Artificial Neural Network Learning: A Comparative Review

Cited by 34 publications

References 31 publications

Machine learning: a review of classification and combining techniques

Machine learning: a review of classification and combining techniques

Downscaling of precipitation for climate change scenarios: A support vector machine approach

Prediction of Breast Cancer Biopsy Outcomes an Approach using Machine Leaning Perspectives

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