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Pattern Recognition I Unsupervised Analysis

It is the last statement in the challenge facing us that distinguishes the [Pg.93]

Meuzelaar and T.L. Isenhour, Computer Enhanced Analytical Spectroscopy , Plenum Press, New York, USA, 1987. [Pg.94]

Although the human brain is excellent at recognizing and classifying patterns and shapes, it performs less well if an object is represented by a numerical list of attributes, and much analytical data is acquired and presented in such a form. Consider the data shown in Table 4.1, obtained from an analysis of a series of alloys. This is only a relatively small data set but it may not be immediately apparent that these samples can be organized into well-defined groups defining the type or class of alloy according to their composition. The data from Table [Pg.97]

It is the last statement in the challenge facing us that distinguishes the techniques studied here from supervised pattern recognition schemes to be examined in Chapter 5. In supervised pattern recognition, a training set is identified with which the parent class or group of each sample is known, and this information is used to develop a suitable discriminant function with which [Pg.98]


Pattern Recognition I Unsupervised Analysis For the test data,... [Pg.117]


See other pages where Pattern Recognition I Unsupervised Analysis is mentioned: [Pg.92]    [Pg.93]    [Pg.95]    [Pg.97]    [Pg.99]    [Pg.103]    [Pg.105]    [Pg.111]    [Pg.113]    [Pg.115]    [Pg.117]    [Pg.119]    [Pg.121]    [Pg.97]    [Pg.99]    [Pg.101]    [Pg.103]    [Pg.105]    [Pg.107]    [Pg.109]    [Pg.111]    [Pg.113]    [Pg.115]    [Pg.119]    [Pg.121]    [Pg.123]    [Pg.125]    [Pg.127]   


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I analysis

Pattern analysis

Pattern recognition

Pattern recognition analysis

Unsupervised

Unsupervised Analysis

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