Multiple Kernel Learning
Consistency of the Group Lasso and Multiple Kernel Learning
... We now extend the previous consistency results to the case of nonparametric estimation, where each group is a potentially infinite dimensional space of functions. Namely, the nonparametric group Lasso aims at estimating ...
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A feature selection method based on multiple kernel learning with expression profiles of different types
... uses multiple kernel learning (MKL) [23, 24] combines a forward feature selection procedure to select the relevant feature subset, eliminate redundant features and select compact feature ...
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Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance level Multimodal Sentiment Analysis
... We present a novel way of extracting fea- tures from short texts, based on the acti- vation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal senti- ment ...
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A Novel Multiple Kernel Learning Based Hyperspectral Imagery Analysis G Swathi & P G K Sirisha
... Multitask Learning as a Multiple Kernel Learning ...the kernel matrix, we present a hierarchical decomposition and a power set based ...
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On the Convergence Rate of lp-Norm Multiple Kernel Learning
... In the seminal work of Lanckriet et al. (2004) it was shown that it is computationally feasible to simultaneously learn a support vector machine and a linear combination of kernels at the same time, if we require the ...
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MKRMDA: multiple kernel learning based Kronecker regularized least squares for MiRNA–disease association prediction
... about kernel-based ...a kernel could be seen as a similarity matrix of miRNAs or dis- ...eases. Kernel based approaches used some base kernels, such as disease semantic similarity or miRNA functional ...
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lp-Norm Multiple Kernel Learning
... cast multiple kernel learning as a general regularized risk minimization problem for arbitrary convex loss functions, Hilbertian regularizers, and arbitrary norm-penalties on ...to multiple ...
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Large Scale Multiple Kernel Learning
... using multiple kernel ...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, ...
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A feature fusion based localized multiple kernel learning system for real world image classification
... classified. Kernel based methods have achieved major success in building nonlinear classi- fiers ...A multiple kernel learning (MKL) framework proposed by Lanckriet et ...single kernel. ...
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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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Multiple Kernel Learning Algorithms
... combine multiple kernels instead of using a single ...from multiple sources (different representations or different feature ...several multiple kernel learning ...using multiple ...
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Structured Sparsity and Generalization
... We present a data dependent generalization bound for a large class of regularized algorithms which implement structured sparsity constraints. The bound can be applied to standard squared-norm regularization, the Lasso, ...
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UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
... machine learning approach that ranked on the first place in the Arabic Dialect Iden- tification (ADI) Closed Shared Tasks of the 2018 VarDial Evaluation ...using multiple kernel ...a kernel ...
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Per-Sample Multiple Kernel Approach for Visual Concept Learning
... cited. Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research ...areas. Multiple kernel learning (MKL) methods have shown great ...
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Nonparametric Sparsity and Regularization
... et al., 2004). In this context efficient algorithms (see Schmidt et al., 2007 and Loris, 2009 and refer- ences therein) as well as theoretical guarantees are now available (see B¨uhlmann and van de Geer, 2011 and ...
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Multi target tracking under occlusion using particle filter and projected gradients
... results multiple kernels are enrolled for these years consider scale change in [3], the color similarities and shape fitness are used for occlusion ...reappears. Multiple kernels centered on high motion ...
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Comparison of fuzzy integral fuzzy measure based ensemble algorithms with the state of the art ensemble algorithms
... Integral Multiple Kernel Learning (De- FIMKL) algorithm as an alternative to algorithmic and algorithm-optimisation hybrid FMs, which aggregates the outputs of SVM classifiers through the use of CFI ...
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Generalized Hierarchical Kernel Learning
... Ensemble Learning (REL) where the goal is to construct an ensemble of conjunctive propositional ...Keywords: multiple kernel learning, mixed-norm regularization, multi-task learning, ...
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Title: A Novel Kernel Based Fuzzy C Means Clustering With Cluster Validity Measures
... Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed by [14] to deal with the classification problems with ...
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Deep Learning in Semantic Kernel Spaces
... the kernel regularizer into a loss function and carry out the neural network training by gradient de- ...a multiple (two) layer architecture of kernel functions, inspired by neural networks, is ...
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