The identification of the liquid drop fingerprint combining support vector machine with clustering method

In order to effectively reduce the time complexity of the recognition algorithm and improve the recognition accuracy and the generalization ability, a new method combining support vector machine with clustering is put forward. After classifying by iterative dynamic clustering method applied on 38 kinds of liquid samples, all liquid samples can be divided into 8 categories, which are then separately trained with the support vector machine method. Experimental results show that the recognition accuracy can be up to 100% among selected samples, together with the reduced computational complexity of training models and the significantly improved recognition efficiency. Compared with the previous model, the generalization capability has been greatly enhanced, with its estimation of generalization performance been cut exceeded 90%.