Anda belum login :: 04 Feb 2023 16:34 WIB
ArtikelSMO-Based Pruning Methods for Sparse Least Squares Support Vector Machines  
Oleh: Zeng, Xiangyan ; Chen, Xue-Wen
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1541-1546.
Topik: machines; SMO - based; sparse; least square; support vector; machines
  • Perpustakaan Pusat (Semanggi)
    • Nomor Panggil: II36
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
    Lihat Detail Induk
Isi artikelSolutions of least squares support vector machines (LS - SVM s) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS - SVM. In this paper, we propose a new pruning algorithm for sparse LS - SVMs : the sequential minimal optimization (SMO) method is introduced into pruning process ; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.
Opini AndaKlik untuk menuliskan opini Anda tentang koleksi ini!

Process time: 0.03125 second(s)