System identifies power grid location from media recordings.
problem Identifying the origin of power distribution grid from media recordings.
method Cascaded SVM and pole-matching classifiers for grid identification.
result Cascaded system improves accuracy by 15.57%.
Algorithm improves SVM classification in non-Euclidean spaces.
problem Limitations of traditional SVM in non-Euclidean spaces.
method Covariance-adjusted SVM using Cholesky Decomposition.
result Cholesky-SVM outperforms traditional SVM in non-Euclidean spaces.
Tropical SVM tackles phylogenomics by classifying multi-locus data.
problem Classifying multi-locus data sets for phylogenetic analysis.
method Proposes tropical support vector machines (SVMs) for phylogenomics, formulated as linear programming problems.
result Developed methods for hard and soft margin tropical SVMs, proving necessary and sufficient conditions for separation.
Paper develops DL methods for signal demodulation in wireless comms.
problem Signal demodulation in wireless communications.
method Proposes DBN-SVM and AdaBoost demodulators using real modulated signals.
result Proposed DBN-SVM and AdaBoost demodulators outperform traditional methods.
The support vector machine (SVM) is a powerful and widely used classification algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof for new insights into the behavior of SVM. These insights provide perhaps unexpected relationships between SVM and two other linear classifier…
Extends linear classification framework to nonlinear SVM-based ranking problems.
problem Maximizing performance on relevant samples in ranking problems.
method Dualization, kernel addition, componentwise dual ascent method.
result General framework for nonlinear classifiers in ranking problems.
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifi…
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.
Improved spoken English intelligibility with computer recognition and feature extraction.
problem Improving spoken English pronunciation and intelligibility.
method Automatic speech recognition using PocketSphinx alignment and feature extraction with SVM classifier probability prediction.
result SVM models achieve 82 percent agreement with human transcriptions, up from 75 percent.
Quantum SVMs outperform classical ones on limited data.
problem Classifying and regressing with limited training data.
method Trained SVMs on D-Wave quantum annealer and compared to classical SVMs.
result Quantum SVMs often generalize better to unseen data.
Paper explains AdaBoost's overfitting resistance from feature learning perspective.
problem Understanding AdaBoost's resistance to overfitting.
method Adopt AdaBoost for base classifiers, then use them as features in SVM.
result Proposes AdaBoost+SVM algorithm to explain overfitting resistance.
OKSVM optimizes RBF kernel hyperparameter for SVMs, improving classification performance.
problem Intrinsic dependence of RBF kernel hyperparameter on SVM performance.
method Gradient descent method for automatic hyperparameter learning and SVM weights adjustment.
result OKSVM outperforms classical SVM regardless of initial RBF hyperparameter values.
Classification is an important topic in statistics and machine learning with great potential in many real applications. In this paper, we investigate two popular large margin classification methods, Support Vector Machine (SVM) and Distance Weighted Discrimination (DWD), under two contexts: the high-dimensional, low-sa…
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection techniques have been successfully used in the SVM to perform automatic variable selec…
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. …
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
Paper compares SVM and Bayesian Logistic Regression for breast cancer diagnosis.
problem Improving breast cancer diagnosis accuracy using machine learning.
method Used SVM, Bayesian Logistic Regression, and K-Nearest-Neighbors for classification.
result SVM outperformed other classifiers, closely matched by Bayesian Logistic Regression.
New NHCAs improve multi-category classification efficiency.
problem Efficient multi-category classification for real-world problems.
method Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM), and Improved GEPSVM (IGEPSVM) with OAA, BT, and TDS approaches.
result TDS-TWSVM outperforms other methods in classification accuracy.
Unified Pin-SVM improves accuracy over existing Pin-SVM model.
problem Difficulty in Pin-SVM model for −1≤τ<0. method Unified Pin-SVM model that solves a QPP for −1≤τ≤1. result Significant improvement in accuracy over existing Pin-SVM model.
Improves SVM speed by 2 orders of magnitude for 12 out of 17 datasets.
problem Slowness of kernel classifiers like SVM for large problems.
method Piecewise linear classifier trained from kernel-based classifier.
result Improves classification speed by up to 2 orders of magnitude.
The paper develops fair SVM algorithms that prevent bias in predictions.
problem Designing fair classifiers that prevent implicit biases.
method Develops iterative algorithms to compute fair linear and kernel SVM's, solving a sequence of relaxations using spectral decomposition.
result Effective in achieving high prediction accuracy while ensuring fairness.
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.
The support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has a serious drawback, that is sensitivity to outliers in training samples. The penalty on misclassification is defined by a convex loss called the hinge loss, and the unboun…
New SVM margin bound improves generalization in machine learning.
problem Improving SVM margin bounds for better generalization.
method Stable sample compression schemes to derive new data-dependent generalization bounds.
result Proves a new optimal SVM margin bound with a log factor improvement.
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.
Deep learning models, especially GRU-SVM, classify malware images with high accuracy.
problem Efficiently detecting and classifying newly released malware.
method Used Malimg dataset, trained CNN-SVM, GRU-SVM, and MLP-SVM models.
result GRU-SVM achieved ~84.92% predictive accuracy.
This paper speeds up OCSSVM training using SMO.
problem Training One-Class Slab SVMs is slow.
method Uses updated SMO to divide large problems into smaller, analytically solvable subproblems.
result Training OCSSVMs scales better with large datasets.
New research shows SVM and related methods can overfit without harm in multiclass classification.
problem Understanding benign overfitting in multiclass classification.
method Analyzing three training algorithms: ERM with cross-entropy, least-squares, and one-vs-all SVM.
result All three algorithms can lead to classifiers that interpolate training data and have equal accuracy under high overparameterization.
Analyzes SVM classifier behavior with different parameters and data types.
problem Tuning SVM parameters for balanced and imbalanced data.
method Behavioral analysis of SVM with different parameters and data types, proposing a novel search algorithm.
result Proposed search algorithm reduces computational time and provides expected kernel function range.
Efficient skin lesion analysis combines deep CNN and handcrafted features.
problem Skin lesion analysis for melanoma detection.
method Combines deep learning and handcrafted features, introduces hybrid features.
result 0.841 score on validation dataset using SVM classifier.
Study on how imperfect labels affect classification methods.
problem Effect of imperfect training labels on classification performance.
method Bounding excess risk for various classifiers under noisy labels.
result Imperfect labels can improve performance of k-NN and SVM classifiers.
The paper explores various SVM regularization methods for better classification in biomedical data.
problem Improving SVM classification in biomedical datasets for better sparsity and interpretability.
method Different types of SVM regularization approaches are considered and tested on synthetic and real biomedical datasets.
result The study finds that certain regularization methods improve the interpretability and sparsity of SVM classifiers in biomedical applications.
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.
This paper evaluates t-SNE and MDS for reducing dimensions in datasets and classifying them with KNN, ENN, and SVM.
problem Reducing dimensions in datasets for better classification performance.
method t-SNE and MDS applied to nine datasets, followed by KNN, ENN, and SVM classification.
result Performance comparison of t-SNE and MDS with KNN, ENN, and SVM.
Quantum SVM uses fewer features for faster training.
problem Training high-dimensional SVMs efficiently.
method Quantum linear programming for sparse SVM training.
result Quantum sparse SVM can be trained in sublinear time.
Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. In such cases, we need to think of robust classification methods able to deal with the "unknown" and properly reject samples belo…
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…
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.
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.
Paper proposes methods to improve SVM classifiers in noisy data scenarios.
problem Improving SVM classifiers when training data contains label noise.
method Mixed Integer Linear and Non Linear models with relabeling and clustering.
result Effective methods improve SVM performance in noisy data scenarios.
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
problem Redundant and time-consuming hyperparameter tuning for SVM classifiers in medical imaging.
method Employed multi-task Bayesian optimization to accelerate hyperparameter search.
result Multi-task Bayesian optimization significantly accelerates hyperparameter search.
A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressiv…
We propose several novel methods for enhancing the multi-class SVMs by applying the generalization performance of binary classifiers as the core idea. This concept will be applied on the existing algorithms, i.e., the Decision Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and Max Wins. Alt…
Unified SVM framework tackles multiclass and multilabel classification.
problem Extending SVM to handle multiclass and multilabel problems.
method Unified framework with class-specific weight vectors and penalizing patterns close to an origin.
result Unified framework achieves competitive performance for multiclass and multilabel classification.
Hyperbolic SVM improves classification in complex networks.
problem Accurately classifying points in hyperbolic space with hierarchical relationships.
method Introducing hyperbolic SVM, a hyperbolic formulation of SVM classifiers.
result Hyperbolic SVM outperforms Euclidean SVM in multi-class prediction tasks.
A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitat…
SVM methods improve tack coat classification in French pavements.
problem Classifying thin tack coat layers in GPR data.
method Inverse Machine Learning approach applied to SVM/SVR methods.
result SVM/SVR methods accurately classify tack coat emulsion proportions.
New Frank-Wolfe algorithm speeds up SVM-type multi-category learning.
problem Improving pattern recognition performance in multi-category SVM learning.
method Developed a new optimization algorithm based on Frank-Wolfe framework for MC-SVM variants.
result Closed-form solutions for direction finding and line search in the Frank-Wolfe framework for MC-SVM.