New IRLS algorithms for SVM fitting via MM approach.
problem Fitting support vector machines (SVMs) via quadratic programming.
method Majorization--Minimization (MM) paradigm for iteratively-reweighted least-squares (IRLS) algorithms.
result IRLS algorithms for SVM risk minimization problems with various losses and penalties.
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.
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.
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.
SVM and linear regression models coincide in high dimensions.
problem Understanding the connection between SVM and linear regression in high-dimensional data.
method Analyzing feature models and proving lower bounds on dimensionality.
result A sharp phase transition in Gaussian feature models, with support vector proliferation occurring only in very high dimensions.
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…
Optimizes test set size for accurate diagnosis using machine learning.
problem Determining the minimum test set size for accurate diagnosis.
method Proposes machine learning methods (LASSO and SVM) to predict optimal test set size.
result SVM achieves 90.4% accuracy with a reduced test set by 35.24%.
A fast multilevel SVM framework tackles large-scale data challenges.
problem High computational complexity and quality vs. performance trade-off in SVMs.
method Generalized fast multilevel framework for regular and weighted SVMs.
result Significant speed up compared to state-of-the-art nonlinear SVM libraries.
In this paper we generalize the framework of the feasible descent method (FDM) to a randomized (R-FDM) and a coordinate-wise random feasible descent method (RC-FDM) framework. We show that the famous SDCA algorithm for optimizing the SVM dual problem, or the stochastic coordinate descent method for the LASSO problem, f…
Paper recovers stochastic volatility from VIX term structure.
problem Consistent modeling of SPX and VIX derivatives.
method Inverts market model of VIX to recover SVM for SPX.
result Recovery of non-negative stochastic volatility function.
Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranki…
In this paper, we propose to (seamlessly) integrate b-bit minwise hashing with linear SVM to substantially improve the training (and testing) efficiency using much smaller memory, with essentially no loss of accuracy. Theoretically, we prove that the resemblance matrix, the minwise hashing matrix, and the b-bit minwise…
A method for interpreting SVMs using polynomial kernels, revealing model complexity.
problem Interpreting SVMs built with truncated orthogonal polynomial kernels.
method Orthogonal Representation Contribution Analysis (ORCA) with normalized Orthogonal Kernel Contribution (OKC) indices.
result The method reveals structural aspects of model complexity not captured by predictive accuracy.
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
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.
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.
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 Lp- and risk consistency from global SVMs. 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.
A new method for causal inference in high-dimensional data using machine learning.
problem Causal inference in high-dimensional observational data.
method Support Points Sample Splitting (SPSS) for efficient double machine learning (DML) in causal inference.
result Deep learning with SPSS and hybrid methods outperform SVM with SPSS in computational efficiency and estimation quality.
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.
Study compares non-parametric models for predicting medical insurance reimbursement delays.
problem Estimating the time-lapse between medical insurance reimbursement.
method Comparative study of four non-parametric regression models (KNNs, SVMs, Decision Trees, Random Forests) using R-squared metric.
result Each model's performance varies with training data size, feature space, and hyperparameters.
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.
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.
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.
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…
This paper improves SVM classification using a differentiable loss function and a gradient method.
problem Improving SVM classification with a differentiable loss function.
method Uses the Huberized Support Vector Machine (HSVM) and Proximal Gradient (PG) method.
result The proposed method converges linearly and supports the solution in finite time.
L3-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 L3-SVMs achieves generalization guarantees and competitive performance. Paper introduces MKL-L0/1-SVM for SVM with (0,1) loss.
problem Optimization of SVM with (0,1) loss function. method MKL framework combined with ADMM algorithm for solving the optimization problem.
result Performance of MKL-L0/1-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.
Study uses shape and surface fitting to classify Parkinson's disease accurately.
problem Early identification of Parkinson's disease from non-degenerative variants.
method Processed SPECT images to compute shape- and surface fitting-based features for classification.
result Support Vector Machine (SVM) classifier achieved 97.29% accuracy.
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
problem High computational and energy costs in machine learning model training.
method Augmented Gaussian Process (AGP-MISO) with multi-source optimization.
result The AGP-MISO method reduces computational time and energy consumption compared to traditional approaches.
Reduced SVM training time by limiting solver time for model selection.
problem Time-consuming parameter tuning in SVM model selection.
method Introduced a stopping criterion for SVM training time during parameter tuning.
result Significantly reduced model selection times by an order of magnitude.
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…
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…
This paper uses SVM to predict stock market trends from financial news.
problem Predicting stock market trends using text mining and sentiment analysis.
method Text mining, sentiment analysis, support vector machine (SVM), parameter optimization.
result SVM models show significant influence of news on stock market, with parameter G having the main effect.
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.