Neural network unsupervised, artificial

ANN artificial neural networks—Unsupervised or supervised classi-... 

R. Goodacre, J. Pygall and D.B. Kell, Plant seed classification using pyrolysis mass spectrometry with unsupervised learning the application of auto-associative and Kohonen artificial neural networks. Chemom. Intell. Lab. Syst., 33 (1996) 69-83. 

Self-Organizing Maps (SOM) (= Kohonen maps, Kohonen artificial neural networks) Kohonen maps are self-organizing systems able to face the unsupervised rather than the supervised problems [Kohonen, 1989, 1990],... 

Artificial neural networks A machine or program for supervised or unsupervised learning based on a layered network of neurons. Normally, a network is trained to best describe a biological or chemical system, in order to classify new systems. Used for pattern recognition in cheminformatics, QSAR, and bioinformatics. 

Abstract. Artificial neural networks (ANN) are useful components in today s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits. 

These various chemometrics methods are used in those works, according to the aim of the studies. Generally speaking, the chemometrics methods can be divided into two types unsupervised and supervised methods(Mariey et al., 2001). The objective of unsupervised methods is to extrapolate the odor fingerprinting data without a prior knowledge about the bacteria studied. Principal component analysis (PCA) and Hierarchical cluster analysis (HCA) are major examples of unsupervised methods. Supervised methods, on the other hand, require prior knowledge of the sample identity. With a set of well-characterized samples, a model can be trained so that it can predict the identity of unknown samples. Discriminant analysis (DA) and artificial neural network (ANN) analysis are major examples of supervised methods. 

Classification using unsupervised artificial neural networks (ANN)... 

Domine and co-workers utilized the family of Adaptive Resonance Theory (ART and ART 2-A) based artificial neural networks for unsupervised and supervised pattern recognition (142,143). The simplest ART network is a vec-... 

Rose, V. S., Macfie, H. J. H., and Croall, I. F. (1991) Kohonen topology-preserving mapping an unsupervised artificial neural network method for use in QSAR analysis. Pharmacochem. Libr. 16,213-216. 

Finally, one class of unsupervised methods is represented by self-organising maps (SOM), or Kohonen maps, named after the Finnish professor Teuvo Kohonen. A SOM is a type of artificial neural network that needs to be trained but does not require labelling of the input vectors. Examples of classification analysis by SOMs in biomedical IR and Raman spectroscopy are given in references. ... 

Self-Organizing Maps (SOMs) or Kohonen maps are types of Artificial Neural Networks (ANNs) that are trained using supervised/unsupervised learning to produce a low-dimensional discretized representation (typically 2-dimensional) of an arbitrary dimension of input space of the training samples (Zhong et al. 2005). 

The most frequently used supervised pattern recognition method is the linear discriminant analysis (LDA), not to be confused with its twin brother canonical correlation analysis (CCA) or canonical variate analysis (CVA). Recently, classification and regression trees (CART) produced surprisingly good results. Artificial neural networks (ANNs) can be applied for both prediction and pattern recognition (supervised and unsupervised). 

Supervised and unsupervised classification for example PCA, K-means and fuzzy clustering, linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), fisher discriminant analysis (FDA), artificial neural networks (ANN). 

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