Results 21 to 30 of about 197 (95)
A Kernelized Unified Framework for Domain Adaptation
IEEE Access, 2019 The performance of the supervised learning algorithms such as k-nearest neighbor (k-NN) depends on the labeled data. For some applications (Target Domain), obtaining such labeled data is very expensive and labor-intensive.
Rakesh Kumar Sanodiya +3 more
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Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the
Jose Antonio Padron-Hidalgo +4 more
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Kernelization in Parameterized Computation: A Survey
Tsinghua Science and Technology, 2014 Parameterized computation is a new method dealing with NP-hard problems, which has attracted a lot of attentions in theoretical computer science. As a practical preprocessing method for NP-hard problems, kernelizaiton in parameterized computation has ...
Qilong Feng +3 more
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Regularization and Kernelization of the Maximin Correlation Approach
IEEE Access, 2016 Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction.
Taehoon Lee +3 more
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Budgeted Passive-Aggressive Learning for Online Multiclass Classification
IEEE Access, 2020 Online multiclass classification is a specific problem of online learning that performs a sequence of multiclass classification tasks given the knowledge of previous tasks. The goal is to make correct predictions for this sequence.
Chung-Hao Wu +2 more
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Progressive Line Processing of Kernel RX Anomaly Detection Algorithm for Hyperspectral Imagery
Sensors, 2017 The Kernel-RX detector (KRXD) has attracted widespread interest in hyperspectral image processing with the utilization of nonlinear information. However, the kernelization of hyperspectral data leads to poor execution efficiency in KRXD.
Chunhui Zhao +3 more
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Constraint Composite Graph-Based Weighted CSP Solvers: An Empirical Study
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceThe Weighted Constraint Satisfaction Problem (WCSP) is a very expressive framework for optimization problems. The Constraint Composite Graph (CCG) is a graphical representation of a given (Boolean) WCSP that facilitates its reduction to a Minimum ...
Orazio Rillo, T. K. Satish Kumar
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Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization
Sensors, 2012 When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s ...
Jing-Yu Yang +6 more
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A Gaussian process approach for contextual bandit-based battery replacement [PDF]
ITM Web of ConferencesThe sharing economy has recently become a distinctive business model in China, offering advantages such as low prices and ease of use. Shared electric vehicles have also become an essential part of urban transportation.
Zhou Tianshi
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PLoS ONE, 2010 Therapies consisting of a combination of agents are an attractive proposition, especially in the context of diseases such as cancer, which can manifest with a variety of tumor types in a single case.
Drew Mellor +3 more
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