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Unformatted text preview: his field of study is trying to accomplish. For example, the goal of machine learning may be to mimic the
'learning' function of the brain, but necessarily the processes the brain uses to learn.
As for the algorithm, since it does not have a convex form, we still face the problem of local minimum, although people have devised other techniques to avoid this dilemma.
In sum, Neural Network lacks a strong learning theory to back up its "success", thus it's hard for people to wisely apply and adjust it. Having said that, it is not an active
research area in machine learning. NN still has wide applications in the engineering field such as in control. BUSINESS APPLICATIONS OF NEURAL NETWORKS
Neural networks are increasingly being used in real- world business applications and, in some cases, such as fraud detection, they have already become the method of
choice. Their use for risk assessment is also growing and they have been employed to visualize complex databases for marketing segmentation. This method covers a wide
range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct
quantitative studies to be carried out without the need for rocket- science expertise.
On the Use of Neural Networks for Analysis Travel Preference Data
Extracting Rules Concerning Market Segmentation from Artificial Neural Networks
Characterization and Segmenting the Business- to- Consumer E- Commerce Market Using Neural Networks
A Neurofuzzy Model for Predicting Business Bankruptcy
Neural Networks for Analysis of Financial Statements
Developments in Accurate Consumer Risk Assessment Technology
Strategies for Exploiting Neural Networks in Retail Finance
Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications
Detecting Payment Card Fraud with Neural Networks
Money Laundering Detection with a Neural- Network
Utilizing Fuzzy Logic and Neurofuzzy for Business Advantage...
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- Winter '13