Results 31 to 40 of about 844,277 (275)
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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Learning Isometric Separation Maps
, 2009 Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensional spaces, often revealing the true intrinsic dimension.
Anderson, David V. +2 more
core +1 more source
Enumeration of convex polyominoes using the ECO method [PDF]
Discrete Mathematics & Theoretical Computer Science, 2003 ECO is a method for the enumeration of classes of combinatorial objects based on recursive constructions of such classes. In the first part of this paper we present a construction for the class of convex polyominoes based on the ECO method.
A. Del Lungo +3 more
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Analytic Combinatorics of Lattice Paths: Enumeration and Asymptotics for the Area [PDF]
Discrete Mathematics & Theoretical Computer Science, 2006 This paper tackles the enumeration and asymptotics of the area below directed lattice paths (walks on $\mathbb{N}$ with a finite set of jumps). It is a nice surprise (obtained via the "kernel method'') that the generating functions of the moments of the ...
Cyril Banderier, Bernhard Gittenberger
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Stats, 2022 In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with ...
Sounak Chakraborty +3 more
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Exact heat kernel on a hypersphere and its applications in kernel SVM
, 2017 Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to ...
Song, Jun S., Zhao, Chenchao
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Network Localization of Fatigue in Multiple Sclerosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks ...
Olli Likitalo +12 more
wiley +1 more source
Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation
Sensors, 2018 The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation
Xiaobo Chen +4 more
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Generalization Performance of Quantum Metric Learning Classifiers
Biomolecules, 2022 Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches.
Jonathan Kim, Stefan Bekiranov
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