Đang chuẩn bị liên kết để tải về tài liệu:
COMPUTER-AIDED INTELLIGENT RECOGNITION TECHNIQUES AND APPLICATIONS phần 3

Không đóng trình duyệt đến khi xuất hiện nút TẢI XUỐNG Tải xuống

Các nguyên mẫu chiết xuất của năm phương pháp khác nhau đã được sử dụng để khởi tạo codebooks LVQ: phương pháp (1) là việc khai thác nguyên mẫu phương pháp được đề xuất trong chương này. Phương pháp (2) và (3) là hai phương pháp được gọi là propinit và eveninit, | Prototype-based Classification 83 The prototypes extracted by five different methods were used to initialize the LVQ codebooks method 1 is the prototype extraction method proposed in this chapter. Methods 2 and 3 are two methods called propinit and eveninit proposed in 13 as the standard initialization methods for the LVQ that choose initial codebook entries randomly from the training data set making the number of entries allocated to each class proportional propinit or equal eveninit . Both methods try to assure that the chosen entries lie within the class edges testing it automatically by k-NN classification. Method 4 is k-means clustering 23 which is also widely used for LVQ initialization 28 29 and obtains prototypes by clustering the training data of each class characters having the same label and number of strokes independently. Finally method 5 is the centroid hierarchical clustering method 23 30 one of the most popular hierarchical clustering algorithms 30 . This is used in the same way as k-means clustering. The first advantage of the proposed extraction method comes out when setting the different parameters for the comparison experiments the number of initial entries must be fixed a priori for the propinit eveninit k-means and hierarchical initialization methods while there is not such a need in the extraction algorithm presented in this chapter. Consequently in order to make comparisons as fair as possible the number of initial vectors for a given codebook to be generated by the propinit and eveninit methods was set to the number of prototypes extracted by the algorithm proposed here for the corresponding number of strokes. In addition the number of prototypes to be computed with k-means and hierarchical clustering algorithms was fixed to the number of prototypes extracted by the method proposed here for the same number of strokes and the same label. In all cases the OLVQ1 algorithm 13 was employed to carry out the training. It must be mentioned that .