Supervised neural network

J.C. Mackay, Probable networks and plausible predictions a review of practical Bayesian methods for supervised neural networks, Technical Report, Cavendish Laboratory, Cambridge, UK, 1995. 

Mateos A, Herrero J, Tamames J, Dopazo J, Supervised neural networks for clustering conditions in DNA array data after reducing noise by clustering gene expression profiles, In Lin SM, Johnson KF, eds., Methods of Microarray Data Analysis II, Boston, Kluwer Academic Publ, pp. 91-103, 2002. 

One final issue to note with supervised neural networks is that, because they fit to the data available to... 

Figure 6 A sample structure of supervised neural network.

Supervised neural networks that use an MSE cost function can use formal statistical methods to determine the confidence of the trained model. The MSE on a validation set can be used as an estimate for variance. This value... 

Building Methodology Using Unsupervised Fuzzy Clustering and Supervised Neural Networks. 

Kireev, D. B., Ros, F., Bernard, P., Chretien, J. R., and Rozhkova, N. (1997) Non-supervised neural networks a new classification tool to process large databases. Comput.-Assisted Lead Find. Optim. [Eur. Symp. Quant. Struct. Act. Relat.], 11th, pp. 255-264. 

One example is the use of an unsupervised form of neural networks to classify wood types through their Raman spectra [58]. In this work, the neural-network approach compares favorably to results obtained previously by other researchers using only individual bands for discrimination. Lewis et al. [59] reported the use of a supervised neural network to classify wood types. Other examples of the use of ANN on Raman spectra can be found for polymers [60] as well as more complicated biological samples [61-63]. It is in these... 

Artificial neural networks (ANNs) are computer-based systems that can learn from previously classified known examples and can perform generalized recognition - that is, identification - of previously unseen patterns. Multilayer perceptions (MLPs) are supervised neural networks and, as such, can be trained to model the mapping of input data (e.g. in this study, morphological character states of individual specimens) to known, previously defined classes. 

A counter-propagation neural network is a method for supervised learning which can be used for predictions. 

In many cases, structure elucidation with artificial neural networks is limited to backpropagation networks [113] and, is therefore performed in a supervised man-... 

As described in the Introduction to this volume (Chapter 28), neural networks can be used to carry out certain tasks of supervised or unsupervised learning. In particular, Kohonen mapping is related to clustering. It will be explained in more detail in Chapter 44. 

Indeed, if the problem is simple enough that the connection weights can be found by a few moments work with pencil and paper, there are other computational tools that would be more appropriate than neural networks. It is in more complex problems, in which the relationships that exist between data points are unknown so that it is not possible to determine the connection weights by hand, that an ANN comes into its own. The ANN must then discover the connection weights for itself through a process of supervised learning. 

Chen et al. (2008) employed a commercial electronic tongue, based on an array of seven sensors, to classify 80 green tea samples on the basis of their taste grade, which is usually assessed by a panel test. PCA was employed as an explorative tool, while fc-NN and a back propagation artificial neural network (BP-ANN) were used for supervised classification. Both the techniques provide excellent results, achieving 100% prediction ability on a test set composed of 40 samples (one-half of the total number). In cases like this, when a simple technique, such as fc-NN, is able to supply excellent outcomes, the utilization of a complex technique, like BP-ANN, does not appear justified from a practical point of view. 

A generalised structure of an electronic nose is shown in Fig. 15.9. The sensor array may be QMB, conducting polymer, MOS or MS-based sensors. The data generated by each sensor are processed by a pattern-recognition algorithm and the results are then analysed. The ability to characterise complex mixtures without the need to identify and quantify individual components is one of the main advantages of such an approach. The pattern-recognition methods maybe divided into non-supervised (e.g. principal component analysis, PCA) and supervised (artificial neural network, ANN) methods also a combination of both can be used. 

Current methods for supervised pattern recognition are numerous. Typical linear methods are linear discriminant analysis (LDA) based on distance calculation, soft independent modeling of class analogy (SIMCA), which emphasizes similarities within a class, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. More advanced methods are based on nonlinear techniques, such as neural networks. Parametric versus nonparametric computations is a further distinction. In parametric techniques such as LDA, statistical parameters of normal sample distribution are used in the decision rules. Such restrictions do not influence nonparametric methods such as SIMCA, which perform more efficiently on NIR data collections. 

KNN)13 14 and potential function methods (PFMs).15,16 Modeling methods establish volumes in the pattern space with different bounds for each class. The bounds can be based on correlation coefficients, distances (e.g. the Euclidian distance in the Pattern Recognition by Independent Multicategory Analysis methods [PRIMA]17 or the Mahalanobis distance in the Unequal [UNEQ] method18), the residual variance19,20 or supervised artificial neural networks (e.g. in the Multi-layer Perception21). 

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

Fritzke, B. (1994) Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Networks 7 1441-1460... 

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