[PDF] Top 20 Linear regression and learning : contributions to regularization and aggregation methods
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Linear regression and learning : contributions to regularization and aggregation methods
... referred to as dynamic data, are also considered to build the reservoir ...period, and include mea- surements at wells such as pressure, oil and water rates, or 4D-seismic related ...related ... Voir le document complet
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Streaming kernel regression with provably adaptive mean, variance, and regularization
... kernel, regression, online learning, adaptive tuning, bandits ...Kernel regression methods can learn such possibly non-linear functions by sharing information gathered across ... Voir le document complet
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Aggregation for Linear Inverse Problems
... Regularization methods replace an ill-posed problem by a family of well-posed prob- ...These methods always involve some parameter measuring the closeness of the regularized and the original ... Voir le document complet
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Some contributions to deep learning for metagenomics
... Deep linear support vector machines 9 The idea to do feature selection using SOM is not ...SOM and label map; 2) if the classes overlap, then add a new feature or replace a feature; 3) if a feature ... Voir le document complet
170
Some contributions to deep learning for metagenomics
... Deep linear support vector machines 9 The idea to do feature selection using SOM is not ...SOM and label map; 2) if the classes overlap, then add a new feature or replace a feature; 3) if a feature ... Voir le document complet
170
Contributions to the problems of classification, regression and study of an inverse problem in finance
... Other methods, which do not belong to any of the three categories ...refer to the positive convolution approximation (PCA) of ...convex linear combination of normal densities to the ... Voir le document complet
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Learning from ranking data : theory and methods
... or linear combina- tions of the observed features, representing efficiently the ...straightforward and extending simple concepts such as that of an average or median in the context of preference data, ... Voir le document complet
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Wasserstein regularization for sparse multi-task regression
... multi-task regression with outlier tasks and outlier features (non-overlapping fea- ...feature and each task. Han and Zhang [2015] propose to learn a tree structure on the features, ... Voir le document complet
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Machine learning: Supervised methods, SVM and kNN
... supervised learning, a set of input variables, such as blood metabolite or gene expression levels, are used to predict a quantitative response variable like hormone level or a qualitative one such as ... Voir le document complet
7
Total Variation regularization enhances regression-based brain activity prediction
... Many methods have been tested for classification or regression of fMRI activation images (Linear Discriminant Analysis, Support/Relevance Vector Machines, Lasso, Elastic net regression ... Voir le document complet
5
Simulated Data for Linear Regression with Structured and Sparse Penalties
... equally to this ...is to integrate structure in Machine Learning methods. Methods recently developed claim that they allow simultaneously to link the computed model to the ... Voir le document complet
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Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
... The main message of [15, 16] is that the EWA with a properly chosen prior is able to deal with the sparsity issue. In particular, [15, 16] prove that such an EWA satisfies a sparsity oracle inequality (SOI), which ... Voir le document complet
25
Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems
... machine learning methods generally require a significant amount of data to build an accurate ...due to the large number of samples required. Therefore, it is chosen to generate a ... Voir le document complet
24
Introduction to hypocoercive methods and applications for simple linear inhomogeneous kinetic models
... , and k∂ x f (t)k ≤ C t 3/2 kf 0 k . The result and the method were first presented in [11] in a more general ...contour methods (see [12]) but what is interesting here is the rate and the ... Voir le document complet
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Convex and non-convex regularization methods for spatial point processes intensity estimation
... Different estimating equations are considered, the regularized Poisson and logistic regression likelihoods, employing adaptive lasso regularization method.. TPR FPR PPV TPR FPR PPV TPR FP[r] ... Voir le document complet
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A linear regression model for marine propeller optimization, prototyping and design
... MODEL AND COMPUTED BY S-PLUS. The direct solution method for linear regression coefficient developed in this work produced much larger percentage errors ...implemented and computed under ... Voir le document complet
6
Contributions to unbiased diagrammatic methods for interacting fermions
... goes to zero factorially like the number of diagrams; this can be seen as a consequence of having a finite radius of ...us to greatly alleviate this reminiscence of the sign problem, leaving us with a sign ... Voir le document complet
181
Multi-level tomography reconstructions with level-set and TV regularization methods
... TV regularization, dis- crete tomography, level-set regularization, inverse ...imaging and material ...has to be re- duced [2]. Many attempts can be found in the literature to find ... Voir le document complet
6
Properties and comparison of some Kriging sub-model aggregation methods
... need to be estimated in practice (Roustant et ...estimation methods like maximum likelihood (Stein 2012) or cross validation (Bachoc 2013, Bachoc et ...Zhang and Wang 2010) are too computationally ... Voir le document complet
27
Regularization Methods in Non-Rigid Registration :II. Isotropic Energies, Filters and Splines
... Unité de recherche INRIA Sophia Antipolis 2004, route des Lucioles - BP 93 - 06902 Sophia Antipolis Cedex France Unité de recherche INRIA Lorraine : LORIA, Technopôle de Nancy-Brabois - [r] ... Voir le document complet
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