Combining feature spaces for classification
Combining feature spaces for classification
复制标题
DOI：
10.1016/j.patcog.2009.04.002
复制
发表时间：
2009-11-01
影响因子：
8
通讯作者：
Girolami, Mark A.
中科院分区：
文献类型：
--
作者：
Damoulas, Theodoros;Girolami, Mark A.
在本文中，我们为多项概率模型提供了变分贝叶斯近似，用于基扩展和核组合。我们的模型在分层贝叶斯框架内有良好的基础，并且能够指导性地结合可用的信息源进行多项分类。所提出的框架能够以多种方式对可能的异构源进行信息集成，从特征扩展的简单求和到内核的加权乘积，并且它被证明可以匹配并在某些情况下优于组合各个分类器的众所周知的集成学习方法。同时，对于我们模型的集成学习方法和完整马尔可夫链蒙特卡罗、Metropolis-Hastings 模型的吉布斯解，该近似值大大减少了所需的 CPU 时间和资源。我们提出了我们提出的框架以及对合成和基准数据集的广泛实验研究，并且首次报告了单个内核的求和与乘积之间的比较，作为构建复合内核矩阵的可能不同方法。 (C) 2009 Elsevier Ltd. 保留所有权利。
In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous Sources in a Multitude of ways, from the simple Summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel Matrix. (C) 2009 Elsevier Ltd. All rights reserved.