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kernel learning

Metric and Kernel Learning Using a Linear Transformation

Metric and Kernel Learning Using a Linear Transformation

... multiple kernel learning (Lanckriet et ...spectral kernel learning (Zhu et al., 2005), manifold-based kernel learning (Bengio et ...and kernel target alignment ...

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Large Scale Multiple Kernel Learning

Large Scale Multiple Kernel Learning

... machine learning toolbox which contains a modified version of SVM light (Joachims, 1999) on 500, 1, 000, 5,000, 10,000, 30,000, 50,000, 100,000, 200, 000, 500, 000, 1,000,000, 2, 000,000, 5,000,000 and 10, 000, ...

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Multi Kernel Learning with Online-Batch Optimization

Multi Kernel Learning with Online-Batch Optimization

... Here we present a MKL optimization algorithm based on stochastic gradient descent that has a guaranteed convergence rate. We directly solve the MKL problem in the primal formulation. By having a p-norm formulation of ...

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lp-Norm Multiple Kernel Learning

lp-Norm Multiple Kernel Learning

... multiple kernel learning as a general regularized risk minimization problem for arbitrary convex loss functions, Hilbertian regularizers, and arbitrary norm-penalties on ...multiple kernel ...

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Multiple Kernel Learning Algorithms

Multiple Kernel Learning Algorithms

... In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information ...

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Consistency of the Group Lasso and Multiple Kernel Learning

Consistency of the Group Lasso and Multiple Kernel Learning

... reproducing kernel Hilbert spaces (RKHS), this procedure turns out to be equivalent to learn the best convex combination of a set of basis positive definite kernels, where each kernel corresponds to one ...

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Variable Sparsity Kernel Learning

Variable Sparsity Kernel Learning

... “Composite Kernel Learning (CKL)” ...single kernel; correspond to the extreme sparse and non- sparse combinations of the given ...Sparsity Kernel Learning” (VSKL) ...

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Generalized Hierarchical Kernel Learning

Generalized Hierarchical Kernel Learning

... Ensemble Learning (REL) where the goal is to construct an ensemble of conjunctive propositional ...multiple kernel learning, mixed-norm regularization, multi-task learning, rule ensemble ...

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Large Scale Online Kernel Learning

Large Scale Online Kernel Learning

... online kernel learning methods is that an online learner usually has to maintain a set of support vectors (SV’s) in memory for representing the kernel-based predictive ...online learning ...

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Empirical Optimal Kernel for Convex Multiple Kernel Learning

Empirical Optimal Kernel for Convex Multiple Kernel Learning

... Abstract—Multiple kernel learning (MKL) aims at learning a combination of different kernels, instead of using a single fixed kernel, in order to better match the underlying ...Optimal ...

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A Representer Theorem for Deep Kernel Learning

A Representer Theorem for Deep Kernel Learning

... Relation to neural networks and deep learning We now come back to the finite sample case in this section and discuss the relation of our representer theorem 1 to two of the most common a[r] ...

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JKernelMachines: A Simple Framework for Kernel Machines

JKernelMachines: A Simple Framework for Kernel Machines

... for learning with ...sophisticated learning-based kernel combination methods such as Multiple Kernel Learning (MKL), and a recently published algorithm to learn powered products of ...

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Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach

Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach

... original kernel matrix with α = ...full-rank learning setting, the algorithm based on the polar geometry compete with the LogDet-KL ...when learning 1000 ...the kernel learning setting, ...

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Multi label Learning Based on Kernel Extreme Learning Machine

Multi label Learning Based on Kernel Extreme Learning Machine

... multi-label learning with large scale class labels has turned out to be the research ...on kernel learning machine in this ...the kernel extreme learning ...

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Kernel-Based Multilayer Extreme Learning Machines for Representation Learning

Kernel-Based Multilayer Extreme Learning Machines for Representation Learning

... Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the ...

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Deep Learning in Semantic Kernel Spaces

Deep Learning in Semantic Kernel Spaces

... the kernel regularizer into a loss function and carry out the neural network training by gradient de- ...of kernel functions, inspired by neural networks, is studied to find the best kernel com- ...

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Comparison of fuzzy integral fuzzy measure based ensemble algorithms with the state of the art ensemble algorithms

Comparison of fuzzy integral fuzzy measure based ensemble algorithms with the state of the art ensemble algorithms

... Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic ...

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Reproducing Kernel Banach Spaces for Machine Learning

Reproducing Kernel Banach Spaces for Machine Learning

... of kernel methods for learning in Banach ...reproducing kernel if and only if the finite matrix (1) is always hermitian and positive ...reproducing kernel for some ...of kernel PCA for ...

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Similarity Learning via Kernel Preserving Embedding

Similarity Learning via Kernel Preserving Embedding

... Nowadays, high-dimensional data can be collected every- where, either by low-cost sensors or from the internet (Chen et al. 2012). Extracting useful information from massive high-dimensional data is critical in different ...

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Fast Kernel Classifiers with Online and Active Learning

Fast Kernel Classifiers with Online and Active Learning

... Many authors have proposed to close the gap with online kernel classifiers by providing larger margins. The Averaged Perceptron (Freund and Schapire, 1998) decision rule is the majority vote of all the decision ...

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