multiple kernel
Multiple Kernel based KNN Classifiers for Vehicle Classification
... training data and the test data by using multiple kernel (i.e. lin- ear,Gaussian, Polynomial). After extracting the feature vector we work out the similarity between each test data to all the train- ing ...
7
Multiple Kernel Learning and Feature Space Denoising
... In kernel methods, the choice of kernel function is criti- cally important, since it completely determines the embed- ding of the data in the feature ...problems, multiple kernels capturing different ...
6
Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval
... Given a set of base kernels (and the associated base feature spaces), the formula- tions in Eq. (3) and Eq. (5) learn distance metrics by weighting the base feature spaces, hence they can also be considered as ...
10
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 ...
47
Cost sensitive online multiple kernel classification
... Online Multiple Kernel Classification (CSOMKC) algorithms, which provide a novel method to address all the above challenges of cost-sensitive online classification (and online anomaly detection) from big ...
17
Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals
... The kernel trick allows data to be nonlinearly mapped to a new higher-dimensional space termed the reproducing kernel Hilbert space (RKHS), where the data are (po- tentially) linearly ...the kernel ...
287
Tensor Decomposition in Multiple Kernel Learning
... datasets, multiple kernel matrices are created by the Gaussian kernel with changing γ in the set of [2 −13 , 2 −11 , 2 −9 , 2 −7 , 2 −5 , 2 −3 , 2 −1 , 2 1 , 2 3 ...
77
Per-Sample Multiple Kernel Approach for Visual Concept Learning
... computing multiple kernel functions for each sample-pair over the training set and (2) optimizing the classifier parameters and the sample-wise kernel weights in an alternate manner, which incurs ...
13
Non-Sparse Multiple Kernel Fisher Discriminant Analysis
... Although many of the above references discuss general loss functions (Lanckriet et al., 2004; Sonnenburg et al., 2006; Kloft et al., 2011), they have mainly been focusing on the binary hinge loss. In this sense, the ...
36
Low-rank matrix factorization in multiple kernel learning
... provided, multiple kernel learning and multiple matrix factorization can be reduced to a straightforward concatenation of multiple features describing the data ...the kernel matrix ...
178
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 ...
38
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 Kernel ...
6
Online Multiple Kernel Similarity Learning for Visual Search
... online kernel similarity learning framework for learning kernel-based proximity functions, which goes beyond the conventional linear distance metric learning ...Online Multiple Kernel ...
15
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 ...
16
Large Scale Multiple Kernel Learning
... using multiple kernel ...WD kernel and to d = 8 for the spectrum kernel fixing the SVMs regularization parameter to C = ...A kernel cache of 1GB was used for ...
35
Multiple Kernel Learning Algorithms
... integrate multiple kernel functions calculated on heterogeneous views of the genome data obtained through different experimental ...single kernel and the unweighted sum of kernels. The assigned ...
58
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 ...
14
Absent multiple kernel learning
... Multiple kernel learning (MKL) optimally combines the mul- tiple channels of each sample to improve classification perfor- mance. However, existing MKL algorithms cannot effectively handle the situation ...
9
A feature fusion based localized multiple kernel learning system for real world image classification
... localized multiple kernel learning (LMKL) framework which computes non-uniform weights for kernels based on their location in the feature space ...single kernel based on SVM, MKL, LMKL, and the ...
11
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 ...
45