Results 21 to 30 of about 187,956 (260)
Further enumeration results concerning a recent equivalence of restricted inversion sequences [PDF]
Discrete Mathematics & Theoretical Computer Science, 2022 Let asc and desc denote respectively the statistics recording the number of ascents or descents in a sequence having non-negative integer entries.
Toufik Mansour, Mark Shattuck
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Frontiers in Neuroscience, 2018 This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike ...
Kan Li, José C. Príncipe
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IEEE Access, 2020 Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures.
Xuejun Zhao +3 more
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Kernel Methods for Surrogate Modeling
CoRR, 2019 This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kernel methods have proven to be efficient in machine learning, pattern recognition and signal analysis due to their flexibility, excellent experimental performance and elegant functional analytic background.
Santin G., Haasdonk B.
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Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
Frontiers in Neuroscience, 2013 The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks.
Jonathan C Tapson +6 more
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Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
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, 2003 Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.
Alexander J. Smola, Bernhard Schölkopf
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Applied SciencesMultivariate time series (MTS) clustering has been an essential research topic in various domains over the past decades. However, inherent properties of MTS data—namely, temporal dynamics and inter-variable correlations—make MTS clustering challenging ...
Sebin Heo +3 more
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A distance-based kernel for classification via Support Vector Machines
Frontiers in Artificial IntelligenceSupport Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative ...
Nazhir Amaya-Tejera +3 more
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Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
Applied Sciences, 2019 Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in
Junjie Lu, Jinquan Huang, Feng Lu
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