การออกแบบและพัฒนากระบวนการเรียนรู้แบบหลายกลุ่มสำหรับซัพพอร์ตเวกเตอร์แมชชีน - นิธิวุฒิ อัศว์ศิวะกุล (จุฬาลงกรณ์มหาวิทยาลัย, 2001)
Support Vector Machines (SVMs), based on statistical learning theory, is gaining recognition for its high accuracy especially when used as a binary classifier. For multiclass problems, SVMs need modification. Two main techniques, which are a standard one-against-the-rest (1-v-r) and one-against-one (1-v-1), were proposed. The former method suffers from long training time while the latter has long evaluation time. A Decision Directed Acyclic Graph (DDAG), a modification of 1-v-1, has the advantage of shorter training time as well as evaluation time than 1-v-1. We present a method of extending SVMs for dealing with multiclass problems. Motivated by the DDAG, we propose Adaptive Directed Acyclic Graph (ADAG): a modified structure of the DDAG that has lower number of decision levels and reduces the dependency on the sequence of node patterns. Thus, the ADAG improves the accuracy of the DDAG while maintaining low computational requirement. The experimental results with problems of 5, 12 and 26 classes show that the ADAG outperforms the DDAG when the number of classes increases.