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kernel-based learning theory

GCS Kernel For SVM-Based Image Recognition

GCS Kernel For SVM-Based Image Recognition

... successful kernel methods that has been derived from statistical learning ...supported kernel. In §2, we introduce and derive the new kernel, we named Geometric Compactly Supported (GCS) ...

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Kernel-Based Nonlinear Signal Processing

Kernel-Based Nonlinear Signal Processing

... Kernel-Based Nonlinear Signal Processing ...discuss kernel-based nonlinear signal ...the theory of reproducing kernel Hilbert spaces (RKHS) and their application to ...

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Kernel-Based Methods for Hypothesis Testing: A Unified View

Kernel-Based Methods for Hypothesis Testing: A Unified View

... Intelligence. Olivier Cappé (olivier.cappe@telecom-paristech.fr) received the M.Sc. degree in electrical engineering from the Ecole Supérieure d’Electricité, Paris, France, in 1990, and the Ph.D. degree in signal ...

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End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

... deep learning community and various methodological improvements have made it possible to learn in a day on a GPU complex models that would have required weeks of computations on a traditional CPU (see, ...models ...

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Improved small molecule identification through learning combinations of kernel regression models

Improved small molecule identification through learning combinations of kernel regression models

... through kernel matrices of the inputs (MS/MS spectra) and outputs (molecular ...model learning happens before the aggregation, as compared to multiple kernel learning using, which happens ...

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BCI Signal Classification using a Riemannian-based kernel

BCI Signal Classification using a Riemannian-based kernel

... new kernel for directly handling covariance matrices in classification ...This kernel could be employed in different applications where covariance matrices are the main ingredients of the feature extraction ...

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Kernel-based nonlinear canonical analysis and time reversibility

Kernel-based nonlinear canonical analysis and time reversibility

... 3 The reversibility hypothesis In this section we are interested in reversible processes, i.e. processes with identical distributional properties in initial and reversed times. Discretized uni- dimensional diffusion ...

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Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes

Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes

... the kernel size due to con- straints such as limited memory or instant ...between kernel size and functionality is an ex- tremely hard ...a kernel size. In this paper, we investigate how machine ...

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Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models

Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models

... non-machine learning based MetFrag [ 18 , 19 ] as well as CFM-ID [ 6 , 7 ], which is the most notable machine learning tool relying on the in silico fragmentation ...multiple kernel ...

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Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

... feature- based methods, the second includes appearance feature-based methods while the last considers all the methods that use both geometric and appearance ...

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A Simple Test of Learning Theory

A Simple Test of Learning Theory

... This highlights a more general point. Theorists typically obtain results in complex models by taking limits. In this case, stochastic fictitious play seems to predict significant differences in behaviour between the ...

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Deep kernel representation learning for complex data and reliability issues

Deep kernel representation learning for complex data and reliability issues

... About structured representation learning: • Introduction of Kernel Autoencoders KAEs that combine deep architectures and operator-valued kernels OVKs to allow autoencoding on complex str[r] ...

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The angular kernel in machine learning for hyperspectral data classification

The angular kernel in machine learning for hyperspectral data classification

... Index Terms— Hyperspectral data, spectral angle, SVM, reproducing kernel, machine learning 1. INTRODUCTION Hyperspectral images are now widely available, owing to the development of remote sensing sensors ...

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Preimage problem in kernel-based machine learning

Preimage problem in kernel-based machine learning

... IN KERNEL-BASED MACHINE LEARNING 11 such as the received signal strength indication (RSSI), the connectivity, the hop count, the time difference of arrival, ...are based on either MDS ...

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Kernel-based sensitivity indices for high-dimensional optimization problems

Kernel-based sensitivity indices for high-dimensional optimization problems

... true kernel-based indices with such estimator would requires too many samples and is therefore not well-suited in our optimization ...estimator based on an incomplete U-statistics [ Yam+18 ] was ...

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Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models

... Discussion In this work, we present a novel approach to introduce ana- tomical or functional information in whole-brain machine learning models. Our procedure combines a priori information about the brain anatomy ...

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Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction

Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction

... 20, avenue des buttes de Coesmes Rennes, France christian.raymond@irisa.fr Abstract—Nowadays, wind power and precise forecasting are of great importance for the development of modern electrical grids. In this paper we ...

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A kernel random matrix-based approach for sparse PCA

A kernel random matrix-based approach for sparse PCA

... problem based on Singular Value ...1995). Based on the well-known power method, (Yuan & Zhang, 2013) introduced an efficient sparse PCA approximation to obtain the exact level of required sparsity, by ...

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Learning from ranking data : theory and methods

Learning from ranking data : theory and methods

... que Moussab, qui j’en suis sûre va conquérir le monde. J’espère que l’on continuera à avoir des discussions toujours aussi enrichissantes. Merci aux générations suivantes, avec los chicos de Argentina Mastane (qui ...

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Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos

Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos

... CNN- based frameworks to obtain good performances in terms of ...transfer learning is a convenient trade-off to improve the performance of affective movie content analysis ...

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