Binary Tree of SVM : A New Fast Multiclass Training and Classification Algorithm  

Oleh:

Liu, Jinbai ; Fei, B.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 696-704.
Topik: ALGORITHM; binary tree; SVM; multiclass training; classification algorithm

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c - BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N - 1 binary classifiers in the best situation (N is the number of classes), while it has log / sub 4 / 3 / ((N+3) / 4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.

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