Results 41 to 50 of about 394,763 (294)
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
doaj +1 more source
Improving Vertex Cover as a Graph Parameter [PDF]
Discrete Mathematics & Theoretical Computer Science, 2015 Parameterized algorithms are often used to efficiently solve NP-hard problems on graphs. In this context, vertex cover is used as a powerful parameter for dealing with graph problems which are hard to solve even when parameterized by tree-width; however,
Robert Ganian
doaj +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between
Novi Quadrianto +3 more
openaire +4 more sources
Turing Kernelization for Finding Long Paths in Graph Classes Excluding a Topological Minor [PDF]
Algorithmica, 2017 The notion of Turing kernelization investigates whether a polynomial-time algorithm can solve an NP-hard problem, when it is aided by an oracle that can be queried for the answers to bounded-size subproblems.
B. Jansen +2 more
semanticscholar +1 more source
Multiple Kernel k-means with Incomplete Kernels [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019 Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernel matrices to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of base kernel matrices are absent.
Gao, Wen +9 more
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
IEEE Access, 2021 For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed.
Miho Ohsaki +7 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source