The analysis of decomposition methods for support vector machines

Chih-Chung Chang; Chih-Wei Hsu

首页> 外文期刊>IEEE Transactions on Neural Networks >The analysis of decomposition methods for support vector machines

【24h】

The analysis of decomposition methods for support vector machines

机译：支持向量机的分解方法分析

获取原文

获取原文并翻译 | 示例

获取外文期刊封面封底 >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

The support vector machine (SVM) is a promising technique for pattern recognition. It requires the solution of a large dense quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, very few methods can handle the memory problem and an important one is the "decomposition method." However, there is no convergence proof so far. We connect this method to projected gradient methods and provide theoretical proofs for a version of decomposition methods. An extension to bound-constrained formulation of SVM is also provided. We then show that this convergence proof is valid for general decomposition methods if their working set selection meets a simple requirement.

机译：支持向量机（SVM）是一种有前途的模式识别技术。它需要解决大型密集的二次规划问题。由于内存限制，传统的优化方法无法直接应用。到目前为止，很少有方法可以解决内存问题，其中一个重要的方法是“分解方法”。但是，到目前为止，还没有收敛证明。我们将此方法与投影梯度方法联系起来，并为一种分解方法版本提供了理论证明。还提供了对SVM的约束约束公式的扩展。然后，我们证明，如果收敛分解证明的工作集选择满足简单要求，则对于一般分解方法是有效的。

著录项

来源
《IEEE Transactions on Neural Networks》 |2000年第4期|P.1003-1008|共6页
作者
Chih-Chung Chang; Chih-Wei Hsu;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
关键词

相似文献

外文文献
中文文献
专利

1. A formal analysis of stopping criteria of decomposition methods for support vector machines [J] . Chih-Jen Lin IEEE Transactions on Neural Networks . 2002,第5期

机译：支持向量机分解方法停止准则的形式分析
2. A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis [J] . Dongxiao Niu, Shuyu Dai Energies . 2017,第3期

机译：改进的粒子群优化算法和最小二乘支持向量机的经验模态分解和灰色关联度去噪短期负荷预测模型
3. Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, ... [J] . Sarah J. Dixon, Richard G. Brereton Chemometrics and Intelligent Laboratory Systems . 2009,第1期

机译：比较以边界法表示的五个常见分类器的性能：欧氏距质心的距离，线性判别分析，二次判别分析，学习向量量化和支持向量机，...
4. GLOBAL CONVERGENCE ANALYSIS OF DECOMPOSITION METHODS FOR SUPPORT VECTOR MACHINES [C] . Norikazu Takahashi, Tetsuo Nishi IEEE International Symposium on Circuits and Systems . 2004

机译：支持向量机的分解方法的全局收敛分析
5. Solving large scale support vector machine problems using matrix splitting and decomposition methods. [D] . Nehate, Girish. 2006

机译：使用矩阵分解和分解方法解决大规模支持向量机问题。
6. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy sensitivity and specificity of linear discriminant analysis logistic regression neural networks support vector machines classification trees and random forests [O] . João Maroco, Dina Silva, Ana Rodrigues, 2011

机译：痴呆症预测中的数据挖掘方法：线性判别分析逻辑回归神经网络支持向量机分类树和随机森林的准确性敏感性和特异性的真实数据比较
7. The Analysis of Decomposition Methods for Support Vector Machines [O] . Chih-chung Chang, Chih-wei Hsu, Chih-Jen Lin 1999

机译：支持向量机的分解方法分析

The analysis of decomposition methods for support vector machines

摘要

著录项

相似文献

相关主题

期刊订阅