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SVM:支持向量机,目前在分类中得到广泛的应用
特征融合:主要用来描述各种不同的特征融合方式,常见的方式有前期融合,就是前面所描述的将各个特征拼接在一起,后期融合本文后面会提到
核函数:SVM遇到线性不可分问题时,可以通过核函数将向量映射到高维空间,在高维空间线性可分
多核学习:在利用SVM进行训练时,会涉及核函数的选择问题,譬如线性核,rbf核等等,多核即为融合几种不同的核来训练。该方法属于后期融合的一种,通过对不同的特征采取不同的核,对不同的参数组成多个核,然后训练每个核的权重,选出最佳核函数组合来进行分类。
给定一些base kernels,比如linear,Polynomial,RBF,Sigmoid,对于每一个,可以指定多组参数,也就是一共有M个base
kernels,我们想用它们的线性组合来作为最终的核函数。通过training,得到这个线性组合中每个kernel的权重d(weight)。最经典的是simpleMKL,GMKL,G即Generalized,最优化方法用的是PGD(Projected
Gradient Descend)。为了改进收敛效果,Vishwanathan又提出SPG-GMKL(Spectral Projected
Gradient),同时提出了多核的product组合。SPG-GMKL也被后来者视作state-of-art。
MKL的经典实现有SimpleMKL,Shogun,SPG-GMKL,SMO-MKL.