Kernel method ≈ Kernel method
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks Open
At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a…
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Quantum Machine Learning in Feature Hilbert Spaces Open
A basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely, to efficiently perform computations in an intractably large Hilbert space. In this Letter we explore some theoretical foundati…
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Kernel density estimation and its application Open
Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histog…
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Support Vector Machines for Classification Open
This chapter covers details of the support vector machine (SVM) technique, a sparse kernel decision machine that avoids computing posterior probabilities when building its learning model. SVM offers a principled approach to problems becaus…
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Machine learning for quantum mechanics in a nutshell Open
Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accuracy of QM at the speed of ML. This hands‐on tutorial introduces the reader to QM/ML models based on kernel learning, an elegant, systematical…
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SVM and SVM Ensembles in Breast Cancer Prediction Open
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction mo…
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Predictive modelling and analytics for diabetes using a machine learning approach Open
Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affecte…
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A kernel-based method for data-driven koopman spectral analysis Open
A data-driven, kernel-based method for approximating the leading Koopmaneigenvalues, eigenfunctions, and modes in problems with high-dimensional statespaces is presented.This approach uses a set of scalar observables (functions that map a …
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A new kernel density estimator for accurate home‐range and species‐range area estimation Open
Summary Kernel density estimators are widely applied to area‐related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, b…
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Representation Learning on Graphs: Methods and Applications Open
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph…
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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics Open
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications…
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The Extreme Value Machine Open
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a hu…
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Multiple Kernel k-means with Incomplete Kernels Open
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 co…
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Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering Open
Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several exte…
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Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) Open
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel …
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Matching Node Embeddings for Graph Similarity Open
Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are…
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A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection Open
Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood…
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Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery Open
Motivation: Despite ongoing cancer research, available therapies are still limited in quantity and effectiveness, and making treatment decisions for individual patients remains a hard problem. Established subtypes, which help guide these d…
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Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models Open
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propos…
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Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach Open
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a b…
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Supervised quantum machine learning models are kernel methods Open
With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circui…
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Support vector machines on the D-Wave quantum annealer Open
Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in comparis…
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Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function Open
In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the security goals. One of the primary challenges to intrusion detection is the problem of misjudgment, misdetection and lack of real t…
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Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning Open
We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of graph Laplacians, and combine labeled and unlabeled data in a sy…
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Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation Open
Wind energy is a kind of sustainable energy with strong uncertainty. With a large amount of wind power injected into the power grid, it will inevitably affect the security, stability and economic operation of the power grid. High-precision…
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Tutorial on PCA and approximate PCA and approximate kernel PCA Open
Principal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario…
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Kernel methods for interpretable machine learning of order parameters Open
Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of …
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Breast Cancer Prediction using varying Parameters of Machine Learning Models Open
Malignancy of tumor has caused major number of deaths among women. Machine learning tools with proper hyper parametric can help in identifying tumors efficiently. This paper presents six supervised machine learning algorithms such as k-Nea…
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Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression Open
To detect multiple sclerosis (MS) diseases early, we proposed a novel method on the hardware of magnetic resonance imaging, and on the software of three successful methods: biorthogonal wavelet transform, kernel principal component analysi…
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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring Open
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically re…