Titlebook: Kernel Learning Algorithms for Face Recognition; Jun-Bao Li,Shu-Chuan Chu,Jeng-Shyang Pan Book 2014 Springer Science+Business Media New Yo

制定法律

Kernel Learning Foundation,el methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data to a high-dimensional feature space.

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Kernel Discriminant Analysis Based Face Recognition,essful in a lot of real-world applications. LDA works well in some cases, but it fails to capture a nonlinear relationship with a linear mapping. In order to overcome this weakness of LDA, the kernel trick is used to represent the complicated nonlinear relationships of input data to develop kernel discriminant analysis (KDA) algorithm.

d-limonene

Kernel Manifold Learning-Based Face Recognition,n aims to develop a meaningful low-dimensional subspace in a high-dimensional input space such as PCA and LDA. LDA is to find the optimal projection matrix with Fisher criterion through considering the class labels, and PCA seeks to minimize the mean square error criterion.

集合

ineptitude

說(shuō)明

978-1-4939-5212-0Springer Science+Business Media New York 2014

thwart

Jun-Bao Li,Shu-Chuan Chu,Jeng-Shyang PanDiscusses the system framework of kernel based face recognition.Introduces a new method of machine learning as it relates to face recognition.Presents algorithms for pattern recognition and machine le

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Lipohypertrophy

Kernel-Optimization-Based Face Recognition,Feature extraction is an important step and essential process in many data analysis areas, such as face recognition, handwriting recognition, human facial expression analysis, speech recognition.