Unsupervised

The underlying learning process can follow different concepts. The two major learning strategies are unsupervised learning and supervised learning. 

Table 9-1 summarizes common methods for unsupervised and supervised learning. 

The Kohonen network i.s a neural network which uses an unsupervised learning strategy. Sec Section 9.5,3 for a more detailed description. 

The Kohonen network adapts its values only with respect to the input values and thus reflects the input data. This approach is unsupervised learning as the adaptation is done with respect merely to the data describing the individual objects. 

Tt provides unsupervised (Kohonen network) and supervised (counter-propagation network) learning techniques with planar and toroidal topology of the network. 

The main characteristics of the method, developed in our group for reaction classification arc 1) the representation of a reaction by physicochemical values calculated for the bonds being broken and made during the reaction, and 2 use of the unsupervised learning method of a self-organi2ing neural network for the perception of similarity of chemical reactions [3, 4],... 

Multiple linear regression is strictly a parametric supervised learning technique. A parametric technique is one which assumes that the variables conform to some distribution (often the Gaussian distribution) the properties of the distribution are assumed in the underlying statistical method. A non-parametric technique does not rely upon the assumption of any particular distribution. A supervised learning method is one which uses information about the dependent variable to derive the model. An unsupervised learning method does not. Thus cluster analysis, principal components analysis and factor analysis are all examples of unsupervised learning techniques. 

Fig. 3. Types of pattern recognition techniques (a) preprocessing, (b) display, (c) unsupervised learning, and (d) supervisediearning.

In unsupervised learning, the outcome is usually a hypothesis to then be tested, often usiag classification or prediction methods. If the unsupervised learning process suggests the presence of distinct clusters, the hypothesis can be tested by applyiag a classification method to the data. A low number of misclassified samples would tend to reinforce the hypothesis. 

Learning can be broadly divided into three categories supervised, unsupervised, and reinforced learning. 

Unsupervised learning—In this type the network is able to discover statistical regularities in its input space and automatically develops different modes of behavior to represent different types of inputs. 

Korolev D, Balakin KV, Nikolsky Y, Kirillov E, Ivanenkov YA, Savchuk NP, Ivashchenko A A, Nikolskaya T. Modeling of human cytochrome p450-mediated drug metabolism using unsupervised machine learning approach. J Med Chem 2003 46 3631-43. 

Whitley DC, Ford MG, Livingstone DJ. Unsupervised forward selection a method for eliminating redundant variables. J Chem Inf Comput Sci 2000 40 1160-8. 

False. Used sensibly, candles need not cause danger. However, they should never be left unsupervised and should only be used on fireproof surfaces and away from anything flammable. 

Shen Q, Ren H, Fisher M, Bouley J, Duong TQ. Dynamic tracking of acute ischemic tissue fates using improved unsupervised isodata analysis of high-resolution quantitative perfusion and diffusion data. J Cereb Blood Flow Metab. 2004 24 887-897. 

As it stands this method can hardly be applied to activities of the scale of interest. Indeed, regarding temperature, pressure and volume the thresholds used are far too critical. The distinction between continuous and discontinuous techniques does not seem of any use in this context. There are a lot of situations that often arise under smaller scale conditions which are missing for instance, glass materials, activities outside working hours or unsupervised handling, etc. 

Analytical results are often represented in a data table, e.g., a table of the fatty acid compositions of a set of olive oils. Such a table is called a two-way multivariate data table. Because some olive oils may originate from the same region and others from a different one, the complete table has to be studied as a whole instead as a collection of individual samples, i.e., the results of each sample are interpreted in the context of the results obtained for the other samples. For example, one may ask for natural groupings of the samples in clusters with a common property, namely a similar fatty acid composition. This is the objective of cluster analysis (Chapter 30), which is one of the techniques of unsupervised pattern recognition. The results of the clustering do not depend on the way the results have been arranged in the table, i.e., the order of the objects (rows) or the order of the fatty acids (columns). In fact, the order of the variables or objects has no particular meaning. 

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