Results 1 to 10 of about 197 (95)
Adaptive kernel fuzzy clustering for missing data [PDF]
PLoS ONE, 2021 Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive ...
Anny K. G. Rodrigues +2 more
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A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease [PDF]
Frontiers in Aging Neuroscience, 2022 With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods,
Solale Tabarestani +18 more
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An overview of kernelization algorithms for graph modification problems
Tsinghua Science and Technology, 2014 Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion ...
Jianxin Wang, Jiong Guo
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Survival parametric modeling for patients with heart failure based on Kernel learning [PDF]
BMC Medical Research MethodologyTime-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis.
Maryam Montaseri +4 more
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Searching and Indexing Genomic Databases via Kernelization [PDF]
Frontiers in Bioengineering and Biotechnology, 2015 The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes.
Travis eGagie, Simon ePuglisi
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Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging [PDF]
Frontiers in Neuroinformatics, 2016 In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables.
Natalia Y Bilenko +2 more
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Expansion Lemma—Variations and Applications to Polynomial-Time Preprocessing
Algorithms, 2023 In parameterized complexity, it is well-known that a parameterized problem is fixed-parameter tractable if and only if it has a kernel—an instance equivalent to the input instance, whose size is just a function of the parameter.
Ashwin Jacob +2 more
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Clique Search in Graphs of Special Class and Job Shop Scheduling
Mathematics, 2022 In this paper, we single out the following particular case of the clique search problem. The vertices of the given graph are legally colored with k colors and we are looking for a clique with k nodes in the graph.
Sándor Szabó, Bogdán Zaválnij
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Algorithms, 2020 This Special Issue contains eleven articles—surveys and research papers—that represent fresh and ambitious new directions in the area of Parameterized Complexity. They provide ground-breaking research at the frontiers of knowledge, and they contribute to
Neeldhara Misra +2 more
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Algorithms for comparing large pedigree graphs
Advances in Computing and Engineering, 2022 The importance of pedigrees is translated by geneticists as a tool for diagnosing genetic diseases. Errors resulting during collection of data and missing information of individuals are considered obstacles in deducing pedigrees, especially larger ones ...
Nahla A. Belal +2 more
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