Localized SVMs maintain SVM's consistency properties for large datasets.
problem Inefficient computational requirements of global SVMs for large data sets.
method Localized SVMs apply different hyperparameters to different regions of the input space.
result Localized SVMs inherit L p L_p L p - and risk consistency from global SVMs. 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…
L 3 ^3 3 -SVMs clusters data, reduces dimensions, and learns linear models.
problem Capturing non-linearities and scaling to large datasets.
method Clusters input space, projects data onto landmarks, learns linear combination of local models.
result L 3 ^3 3 -SVMs achieves generalization guarantees and competitive performance. The paper improves SVM and localized SVM stability under triple perturbations.
problem Stability of SVMs and localized SVMs under triple perturbations.
method Generalizes and improves existing results, considering simultaneous variations in probability measure, regularization parameter, and kernel.
result Improved stability of SVMs and localized SVMs under triple perturbations.
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…
GADGET SVM uses gossip-based distributed learning for scalable SVMs.
problem Scalability issues in traditional SVM algorithms for large datasets.
method Gossip-based distributed learning for the primal SVM formulation.
result Performance comparable to centralized and online SVM algorithms.
Proposes SVM-based Deep Stacking Network for improved deep learning.
problem Improving deep learning performance and interpretability.
method Uses stacked SVM classifiers within a DSN architecture and a BP-like layer tuning scheme.
result Demonstrates superior performance compared to benchmark models on image and text data.
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…
SVM with local features improves human action recognition.
problem Improving human action recognition in videos.
method Local appearance and motion features extracted using CNNs, concatenated, and used with SVM for classification.
result SVM with local features outperforms previous methods on benchmark datasets.
Novel defense algorithm improves SVMs against data poisoning attacks.
problem Vulnerability of SVMs to targeted training data manipulations like poisoning attacks.
method Developed a weighted SVM using K-LID to de-emphasize suspicious data samples.
result Significant reduction in classification error rates (10% on average) with the proposed defense.
FDR-SVM improves classification robustness in federated learning with uncertain data.
problem Federated learning with uncertain and private client data.
method Develops FDR-SVM, a robust SVM approach using a mixture of Wasserstein balls ambiguity set.
result Establishes theoretical guarantees and derives algorithms with performance bounds.
Improved SVMs learn from few samples with composition and multiple scales.
problem Learning with small sample sizes.
method Transformation-invariant SVMs with composition and locality at multiple scales.
result Kernels based on maximum similarity are positive definite and yield superior accuracy.
Paper analyzes localized SVMs for robustness and consistency.
problem Handling large datasets efficiently and robustly.
method Localized support vector machines (SVMs) for non-parametric learning.
result Locally learnt kernel methods are universal consistent and robust.
Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with…
Efficiently solves large-scale SVMs with sparse semismooth Newton method.
problem Numerical difficulties in solving large-scale SVMs.
method Sparse semismooth Newton based augmented Lagrangian method.
result Outperforms state-of-the-art solvers for large-scale SVMs.
EDSVM uses elite observations to guide SVM classification.
problem Classical SVMs lack ways to encode trusted models or preferences.
method EDSVM augments SVMs by guiding slack variables for elite observations.
result EDSVM models closely track reference SVMs while achieving competitive performance.
Improved SVMs handle large datasets more efficiently and robustly.
problem Handling large datasets in SVMs for runtime and storage.
method Developed a locally learned predictor using influence function analysis.
result The locally learned predictor is differentiable and robust to distribution changes.
Optimizes one-class classification methods for better performance.
problem Improving one-class classification accuracy through hyperparameter optimization.
method Hyperparameter optimization for five one-class classification methods (SVM, NND, LNND, LOF, ALP) using various datasets.
result ALP and SVM perform best after hyperparameter optimization, with ALP being more efficient.
LS-SVM performance analyzed for large p and n, showing Gaussian approximation.
problem Performance analysis of LS-SVM in high-dimensional settings.
method Random matrix theory applied to LS-SVM under Gaussian mixture model.
result LS-SVM decision function approximates normally distributed under large p and n.
A new method for high-dimensional classification using Bernstein polynomials.
problem Computational difficulties in high-dimensional SVM hinge loss.
method Proposes Bernstein support vector machine (BernSVM) and two efficient algorithms.
result Achieves a prediction accuracy rate of s log ( p ) / n \sqrt{s\log(p)/n} s log ( p ) / n with high probability. We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-…
Paper proposes semi-supervised method for dictionary learning.
problem Learning from both labeled and unlabeled data.
method Uses semi-supervised dictionary learning with LLE for manifold preservation.
result Significant improvements over other methods demonstrated.
New algorithms parallelize all-in-one SVM training, improving text classification accuracy.
problem Training all-in-one SVMs is computationally infeasible for many classes.
method Developed distributed algorithms for two all-in-one SVM formulations.
result Superior accuracy on text classification data compared to one-vs.-rest SVMs.
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
problem One-class SVM with privileged information
method Sequential Minimal Optimization (SMO) algorithm
result Finite-time convergence established
Unified Pin-SVM improves accuracy over existing Pin-SVM model.
problem Difficulty in Pin-SVM model for − 1 ≤ τ < 0 -1 \leq τ< 0 − 1 ≤ τ < 0 . method Unified Pin-SVM model that solves a QPP for − 1 ≤ τ ≤ 1 -1 \leq τ \leq 1 − 1 ≤ τ ≤ 1 . result Significant improvement in accuracy over existing Pin-SVM model.
MU-SVM improves multiclass classification accuracy.
problem Multiclass classification problems.
method Proposes MU-SVM for multiclass learning and an analytic span bound for model selection.
result Achieves > 20% improvement in test accuracies compared to multi-class SVM.
BAEN-SVM improves SVM robustness to noisy data.
problem Noise and geometric irrationalities in SVM.
method Bounded asymmetric elastic net loss combined with SVM.
result BAEN-SVM is robust to noise and geometrically well-defined.
This work extends SVM error bounds to weighted SVM and introduces hyperparameter selection methods.
problem Improving SVM performance through effective hyperparameter selection.
method Extending span error bound theory to weighted SVM and introducing hyperparameter selection methods.
result The span rule is the most effective method for weighted SVM hyperparameter selection and provides the best predictor of test error.
New SVM feature selection methods improve wafer testing accuracy.
problem Need for improved SVM feature selection methods.
method Entropy measurement and K-medoid clustering for feature selection.
result One-class SVM's enhance 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.
problem Big data challenge in SVM classification.
method Improved indirect sampling technique for LS-SVM.
result Algorithm achieves logarithmic runtime for low rank data matrices.
This paper improves SVM prediction uncertainty quantification methods.
problem Lack of comprehensive UQ methods for SVM predictions.
method Developed SSVQR model for sparse PI estimation and feature selection algorithm.
result Proposed SSVQR model achieves sparse solutions and improves PI quality.
Introduces Soft-SVM for binary classification bridging logistic and SVM.
problem Data separability issues in binary classification.
method Soft-SVM regression using convex relaxation of hinge loss with softness and class-separation parameters.
result Soft-SVM performs well in classification and prediction errors.
Optimal posterior distributions improve SVM classifiers and parameter selection.
problem Improving SVM classifiers and selecting optimal regularization parameters.
method PAC-Bayesian approach with optimal posterior identification for stochastic classifiers.
result Optimal posteriors yield tight risk bounds and improved SVM performance.
Paper addresses SVM bias in high-dimension, low-sample-size settings.
problem Bias in SVM performance in high-dimension, low-sample-size settings.
method Proposes a bias-corrected SVM (BC-SVM) to improve SVM performance.
result BC-SVM gives preferable performances in high-dimension, low-sample-size settings.
Paper proposes an ensemble SVM method for efficient VAD.
problem Efficient and accurate VAD for speech processing.
method Supervised learning with ensemble SVM on large datasets.
result Ensemble SVM outperforms stand-alone SVM in VAD accuracy.
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…
Novel SVM approach for extreme quantile regression with heavy tailed inputs.
problem Learning from extreme values in quantile regression.
method Support Vector Machine framework for handling high-dimensional and nonlinear settings.
result Established finite-sample learning guarantees under mild regularity assumptions.
Paper introduces MKL- L 0 / 1 L_{0/1} L 0/1 -SVM for SVM with ( 0 , 1 ) (0, 1) ( 0 , 1 ) loss.
problem Optimization of SVM with ( 0 , 1 ) (0, 1) ( 0 , 1 ) loss function. method MKL framework combined with ADMM algorithm for solving the optimization problem.
result Performance of MKL- L 0 / 1 L_{0/1} L 0/1 -SVM comparable to SimpleMKL. The paper uses KKT conditions to reveal new insights into SVM behavior.
problem Understanding SVM behavior and tuning.
method Using Karush-Kuhn-Tucker conditions to explore SVM connections with other classifiers.
result SVM can be seen as a cropped version of mean difference and maximal data piling direction classifiers.
SVM used for estimating treatment effects without confounding.
problem Estimating average treatment effects in the presence of confounding variables.
method Adapts SVM classifier as a kernel-based weighting procedure to balance covariates and estimate causal effects.
result SVM provides a continuous relaxation of the quadratic integer program for balancing covariates and maximizing effective sample size.
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
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…
Quantum LS-SVM simplifies matrix inversion for faster machine learning.
problem Speeding up machine learning algorithms for large datasets.
method Introduces a novel quantum algorithm using continuous variables to simplify matrix inversion in LS-SVM, and proposes a hybrid quantum-classical approach for sparse solutions.
result Quantum LS-SVM achieves exponential speed-up and can solve classically difficult tasks.
The study analyzes LS-SVM performance using random matrix theory.
problem Performance analysis of LS-SVM in large dimensional settings.
method Random matrix theory applied to LS-SVM performance analysis.
result The LS-SVM decision function is asymptotically normal.
SVM predicts economic recessions in real-time.
problem Determining the onset and end of recessions quickly.
method Support Vector Machines (SVM) applied to nowcasting.
result SVM achieves excellent predictive performance for nowcasting recessions.
Stepwise SVM reduces dimensions of large p small n datasets effectively.
problem Reducing dimensions of large p small n datasets.
method Stepwise support vector machine (SVM) for dimension reduction.
result Stepwise SVM achieves better prediction performance and is more stable than other methods.
Paper examines M-SVM for multi-task learning, showing reliability and pre-convergence-rate factor improvements.
problem Whether MTL always provides reliable results and how MTL outperforms independent learning.
method Regularized multi-task learning (MTL) based on SVM models (M-SVM).
result M-SVM is Bayes risk consistent in large sample size, improving pre-convergence-rate factor (PCR) for small data.