Recognition accuracy

For pattern recognition or classification, usually, the following characteristics of recognition accuracy are used (see, for example, refs. 66,106-108) ... 

The recognition accuracy estimation described above faces one very important problem what is the best choice for the threshold value 0 To solve this problem, statistical decision theory is used. ° The basis for this is an analysis of the so-called the Received Operating Characteristic (ROC) curve. By tradition, ROC is plotted as a function of true positive rate TPj TP + FN) (or sensitivity) versus false positive rate FPj TN+FP) (or 1-Specificity) for all possible threshold values 0. Figure 6.5 presents an example of such a ROC curve for the results obtained with our computer program PASS in predicting antineoplastic activity. 

In any case, this approach uses several additional assumptions. For this reason in the last time in ML the recognition accuracy criterion of the Area Under the ROC Curve AUC), which is free of additional assumptions, becomes very popular. Mathematically, AUC equals the... 

S. Di Carlo, M. Falasconi, E. Sanchez, A. Scionti, G. Squillero, A. Tonda, Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation. Pattern Recogn. Lett. 32(13), 1594-1603 (2011)... 

Culverhouse, P.F., Williams, R., Reguera, B. and Herry, V. (2003) Expert and machine discrimination of marine flora a comparison of recognition accuracy of field-coUected phytoplankton. Institution of Electrical Engineers International. Conference on Vision Information Engineering, May 23-25 Guildford. UK, pp. 177-183. 

Table 12.6 shows results for different numbers of hidden nodes ranging between 64 and 104. At the point of training termination, the error (E i) and recognition accuracy (R i) produced by the... 

Window Size Test I Test 2 Test 3 Vowel recognition accuracy (%)... 

Table 2 Confusion matrix of the highest vowel recognition accuracy...

The speech recognition accuracy of TDNN showed higher recognition rate than that of the MLP used by different previous studies. This proved that TDNN had better performance in dealing with the vowel recognition. However, the recognition rate of /i/ and /o/ was lower than the rest of the vowels. 

The power of personal computers is such today that voice recognition is a viable tool and is commercially available. Voice recognition in itself is a complex program. The computer converts an electrical signal into words. To achieve high recognition accuracy, voice recognition software relies on several sources of information ... 

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