arXiv research

Improved deep learning for action recognition using multi-kernel SVM and deep neural networks.

Develops multi-kernel regression for graph signal processing.

A new multi-kernel RBFNN design improves performance and speed.

Paper improves generalization bounds for multi-kernel learning with mixed datasets.

Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.

Paper introduces a new multi-kernel algorithm for better gradient approximation.

Novel adaptive multi-kernel learning scheme for dynamic environments.

New method preserves privacy while improving machine learning accuracy.

The paper develops a multi-kernel method with sparsity constraint for regression.

Combining distributed Gaussian Processes with deep CNNs improves performance on action recognition.

SimpleMKKM improves multi-kernel clustering efficiency.

MKCapsnet improves schizophrenia identification using multi-kernels and dropout.

A new kernel function centers at different points improves robust learning.

Enhanced LSTM with multiple kernels and attention improves video action recognition.

MKPN predicts varying-sized kernels for burst image denoising.

Paper develops a graph-based method for reconstructing spatio-temporal signals.

The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…

Localized Multiple Kernel Learning improves anomaly detection performance.

Improved neural network accuracy with multi-Kernel activation functions.

Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.

Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metr…

Unified Pin-SVM improves accuracy over existing Pin-SVM model.

Localized SVMs maintain SVM's consistency properties for large datasets.

MU-SVM improves multiclass classification accuracy.

BAEN-SVM improves SVM robustness to noisy data.

This work extends SVM error bounds to weighted SVM and introduces hyperparameter selection methods.

New SVM feature selection methods improve wafer testing accuracy.

Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus…

A quantum-inspired classical algorithm speeds up LS-SVM classification.

This paper improves SVM prediction uncertainty quantification methods.

Introduces Soft-SVM for binary classification bridging logistic and SVM.

In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM holds a consistency property in which misclassification rates tend to zero as the dimension goes to infinity under certain severe conditions. …

Paper proposes an ensemble SVM method for efficient VAD.

GADGET SVM uses gossip-based distributed learning for scalable SVMs.

Proposes SVM-based Deep Stacking Network for improved deep learning.

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information avai…

Paper introduces MKL-$L_{0/1}$-SVM for SVM with $(0, 1)$ loss.

Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way. Given enough computational resources, one-vs.-rest SVMs can thus be trained on data involving a large number of classes. The same cannot be stated, however, for the so-called all-in-one SVMs, which require solving a …

SVM used for estimating treatment effects without confounding.

SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.

We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \infty]. The new B-SVM objective function contains a penalty term that encourages the decision rule to lie in a user specif…

One of the limiting factors of using support vector machines (SVMs) in large scale applications are their super-linear computational requirements in terms of the number of training samples. To address this issue, several approaches that train SVMs on many small chunks of large data sets separately have been proposed in…

In support vector machine (SVM) applications with unreliable data that contains a portion of outliers, non-robustness of SVMs often causes considerable performance deterioration. Although many approaches for improving the robustness of SVMs have been studied, two major challenges remain in robust SVM learning. First, r…

Quantum LS-SVM simplifies matrix inversion for faster machine learning.

SVM predicts economic recessions in real-time.

Stepwise SVM reduces dimensions of large p small n datasets effectively.

Paper examines M-SVM for multi-task learning, showing reliability and pre-convergence-rate factor improvements.

The paper improves SVM and localized SVM stability under triple perturbations.