Open AccessBook
Kernel Learning Algorithms for Face Recognition
TLDR
This book discusses the advanced kernel learning algorithms and its application on face recognition and focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition.Abstract:
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.read more
Citations
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Journal ArticleDOI
The parameter sensitivity of random forests
TL;DR: There is significant benefit to be gained by model tuning RFs away from their default parameter settings, and parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs).
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Decentralized Online Learning With Kernels
TL;DR: This work proposes an algorithm that allows each individual agent to learn a regression function that is close to the globally optimal regression function, and establishes that with constant step-size selections agents’ functions converge to a neighborhood of the global optimal one while satisfying the consensus constraints as the penalty parameter is increased.
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Overview of principal component analysis algorithm
TL;DR: Algorithm implementation of the original PCA algorithm and main extended-PCA algorithms including two-dimensional PCA, 2DPCA-based feature fusion approach, the kernel PCA (KPCA, the modular PCA), the improved KPCA (IKPCA), efficient sparse KPC a (ESK PCA) and incremental PC a (IPCA) are presented.
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Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers
TL;DR: A state determination method that finds a target system state for a quantum computer at a given target objective function value is proved and is convenient for gate-model quantum computations and the near-term quantum devices of the quantum Internet.
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Hyper)Graph Embedding and Classification via Simplicial Complexes
TL;DR: This paper investigates a novel graph embedding procedure based on simplicial complexes and proposes two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy.