THOMAS L. ISENHOUR Department of Chemistry University of North Carolina Chapel Hill, N.C. 27514 PETER C. JURS Department of Chemistry The Pennsylvania State University University Park, Pa. 16802
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INTERPRETATION
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mental data and the correspond ing establishment of cause and ef fect relationships are essential as pects of experimental chemistry. I n general, the investigator has d a t a which he wishes to place into cer tain categories. For example, infra red spectra can be used to place compounds into categories defined by functional groups, or pKa values can be used to define the degrada tion products of certain protein re actions. Placing data into specific categories, then, is often the basis of interpretation of experimental re sults. Two approaches can be used to relate data to categories—theoreti cal or empirical. Theoretical data interpretation is usually preferred because it is based on explicit causal relationships derived from earlier observations or from logi cally constructed models. T h a t is, scientists normally prefer interpre tations based on theory because they feel they understand the mea surement process in some or even all aspects. However, not even the most ardent theoretician would be likely to a t t e m p t the interpretation of the dc arc emission spectra of an iron alloy starting from first princi 20 A
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Some Chemical Applications of Machine Intelligence ples. Empirical methods are, how ever, readily applied in m a n y com mon analytical situations; and, most frequently, some combination of the theoretical and empirical a p proaches is used. For example, while most scientists are satisfied with current theories of light ab sorption by molecules, it is standard procedure to measure the spectrum of a new compound and select a de sirable absorption wavelength em pirically in order to develop a colorimetric method. The learning machine method, presented here, is a totally empirical method of data interpretation. T h e sole assumption is t h a t a relation ship between the d a t a and the de fined categories exists—i.e., the ex periment measured something re lated to the property of interest. Even this assumption will be in vestigated by the empirical method itself. Hence, the learning machine method does not depend upon es tablished theory and, while it is dis advantageous in t h a t accepted hy potheses m a y not be used, it is si multaneously advantageous in t h a t interpretation will not be restricted to current accepted schools of thought. T h e term "learning" used in this
ANALYTICAL CHEMISTRY, VOL. 43, NO. 10, AUGUST 1971
context refers to a decision process which improves performance of a task as its experience at performing the task increases. T h e application of negative feedback causes the de cision process to be modified to dis criminate against wrong answers, therefore improving its performance with time. I n general, empirical re lationships are established between available inputs and desired out puts. In this article the inputs will be chemical measurements and the outputs will be the previously men tioned data categories. Pattern Recognition
Starting in the late 1940's a great m a n y books, papers, and conference reports have dealt with the various phases of the theory, design, devel opment, and use of learning ma chines (1—13). Such studies have been the province of applied m a t h ematicians, statisticians, computeroriented engineers, and others in several disciplines investigating bi ological behavior on the neural level. A recent review by N a g y (14) demonstrates the amorphous nature of the subject. Applications have appeared in such divergent scientific areas as character recog nition (alphabetic and numeric),
