Support spinor machine extends SVM to handle spinor fields in time series data.
problem Handling nonstationary and nonlinear time series data for classification.
method Using wedge product to extend vector fields to spinor fields, extending SVM to support spinor machine.
result Support spinor machine outperforms SVM in one class classification of physiological time series data.
Statistical learning theory explains SVMs for data-driven decision making.
problem Decision making and model construction from data.
method Statistical learning theory, focusing on empirical and structural risk minimization.
result Support Vector Machines (SVMs) are a prominent implementation of structural risk minimization.
Introduces STTM to improve STM's performance.
problem Overfitting and curse of dimensionality in SVM.
method Replaces rank-one tensor in STM with tensor train.
result STTM outperforms SVM and STM.
Improved fuzzy support vector machine for stock price trend forecasting.
problem Weak performance of traditional support vector machines in handling fuzzy and noisy data.
method Proposed a novel advanced fuzzy support vector machine (NA-FSVM) to improve precision.
result Improved model precision in predicting stock price trends.
The paper proposes an algorithm to enumerate K best models with distinct support vectors for SVM.
problem Finding multiple models with distinct support vectors for non-standard machine learning applications.
method A K-best model enumeration algorithm for SVM that efficiently finds models with distinct support vectors in the dual SVM problem.
result The algorithm efficiently finds the next best model with small latency, useful for interactive examination of requirements.
Proposes MLPSVM for multi-label learning, improving on binary relevance.
problem Handles multi-label learning tasks more efficiently than binary relevance.
method Uses standard support vector machines with parallel decision hyper-planes.
result Outperforms other multi-label learning algorithms on various data sets.
SMM preserves matrix data structure for SVM classification.
problem Preserving spatial correlations in matrix data for SVM.
method SMM uses spectral elastic net combining nuclear and Frobenius norms.
result SMM improves SVM performance on matrix data.
Support vector machines have attracted much attention in theoretical and in applied statistics. Main topics of recent interest are consistency, learning rates and robustness. In this article, it is shown that support vector machines are qualitatively robust. Since support vector machines can be represented by a functio…
Minimal SVM reduces support vectors for better classification.
problem Finding optimal hyperplane for classification with fewer support vectors.
method Proposes a Minimal SVM using L0.5 norm on slack variables.
result Increases classification performance by reducing support vectors.
In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient messa…
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.
Proposes algorithm to explore entire regularization path for asymmetric-cost SVMs.
problem Predictive power of SVMs depends on regularization parameters.
method Develops algorithm to explore entire regularization path for asymmetric-cost linear SVMs.
result Demonstrates two-dimensional regularization paths for linear SVMs.
Proposes a new kernel technique for tensor data in SVM.
problem Handling tensorial data in machine learning.
method Kernelized support tensor train machine for image classification.
result Tensorizes the standard SVM on its input structure and kernel mapping scheme.
Improved video tracking accuracy with active learning.
problem Efficiently predicting object matches in videos with active learning.
method Adversarial approach for active learning with structured prediction domains.
result Better accuracy and computational efficiency for object tracking.
Quantum SVM clustering speeds up big data analysis.
problem Performance degradation of classical SVM clustering on big data.
method Developed a quantum version of SVM clustering using quantum support vector machine and kernels.
result Significant speed-up gain on run-time complexity.
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …
Develops a new tensor classification method for high-dimensional data.
problem Efficient learning algorithms exploiting tensorial structure in high-dimensional multi-way arrays.
method Tensor Train Multi-way Multi-level Kernel (TT-MMK) combining Canonical Polyadic decomposition, Dual Structure-preserving Support Vector Machine, and Tensor Train approximation.
result The TT-MMK method provides higher prediction accuracy and is more reliable computationally compared to other techniques.
A new method uses SVMs and active learning for efficient fragility curve estimation.
problem Estimating fragility curves for structures under seismic and other excitations.
method Support Vector Machines (SVMs) coupled with active learning algorithm.
result Efficient estimation of fragility curves with reduced numerical calculations.
SVM generalizes well even with many support vectors in high dimensions.
problem Generalization of SVM in high-dimensional spaces with many support vectors.
method Identified new deterministic equivalences and proved conditions for support vector proliferation.
result Broadened conditions for SVM generalization in high-dimensional settings and proved converse result.
New algorithms reduce computational burden for principal support vector machines.
problem High computational cost of principal support vector machines for large datasets.
method Two distributed estimation algorithms for principal support vector machines.
result Statistical efficiency is maintained with distributed algorithms.
Rgtsvm speeds up SVMs on GPUs for big data.
problem Scaling SVMs to large datasets.
method GPU acceleration for SVM classification and regression.
result Up to 100x performance improvement for millions of examples.
Dual SVM training with budget constraint for faster accuracy.
problem Efficient support vector machine training with limited resources.
method Dual subspace ascent algorithm with budget constraint.
result Significant speed-up over primal budget training methods.
GVM replaces SVM with general project vectors and a Monte Carlo algorithm for improved feature extraction.
problem Improving feature extraction and reducing overlearning in SVM.
method Replaces support vectors with general project vectors and uses a Monte Carlo algorithm to find them.
result The GVM can achieve better performance, especially for small-set training problems.
Sparse LR-LSSVM improves kernel machine performance.
problem Improving kernel machine performance with controlled model size.
method Introduces LR-LSSVM with low rank kernels and a two-step optimization algorithm.
result Proposed algorithm's performance is comparable or superior to existing kernel machines.
LIBTwinSVM offers a free library for efficient Twin Support Vector Machines.
problem Large-scale classification problems.
method Efficient implementation of Twin Support Vector Machines.
result Effectiveness demonstrated through benchmarks.
New methods improve accuracy and scalability for large datasets in multi-class classification.
problem Improving accuracy and scalability for multi-class classification with large datasets.
method Randomized block kernel matrices for approximation of least-squares support vector machines.
result The proposed methods provide good accuracy and reliable scaling for multi-class classification problems with large data sets.
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.
Distributed SVM algorithm improves performance in real-world applications.
problem Improving SVM performance in distributed computing environments.
method Proposes HPSVM, a distributed SVM algorithm that minimizes inter-machine communications.
result HPSVM achieves similar or better results than state-of-the-art SVM techniques.
New algorithm uses machine learning to predict high-frequency trading returns.
problem Improving prediction accuracy in high-frequency trading.
method Iterative optimization and activation functions in deep learning, combined with VPIN, GARCH, and SVM. result The model significantly improved prediction of market liquidity and trading returns.
Training structured prediction models is time-consuming. However, most existing approaches only use a single machine, thus, the advantage of computing power and the capacity for larger data sets of multiple machines have not been exploited. In this work, we propose an efficient algorithm for distributedly training stru…
In this paper we solve support vector machines in reproducing kernel Banach spaces with reproducing kernels defined on nonsymmetric domains instead of the traditional methods in reproducing kernel Hilbert spaces. Using the orthogonality of semi-inner-products, we can obtain the explicit representations of the dual (nor…
Algebraic multigrid speeds up SVM training for large datasets.
problem Computational inefficiency of support vector machines on large-scale data.
method Inspired by algebraic multigrid, a fast multilevel framework for solving SVM models.
result Significant improvement in running time achieved without loss in quality.
Bayesian SVM method speeds up big data predictions.
problem Efficiently predicting with big data and uncertainty.
method Stochastic variational inference and inducing points.
result Faster than competing Bayesian methods, scalable to millions of data points.
A new method classifies color images using quaternion algebra.
problem Classifying color images with preserved intrinsic relationships.
method LSQMM model with quaternion nuclear norm regularization and ADMM algorithm.
result LSQMM outperforms state-of-the-art methods in classification accuracy and efficiency.
A new SVM method for predicting time series labels.
problem Learning to predict labels from high-dimensional time series data.
method Extended SVM concept to continuous time series data, formulated as a convex optimization problem.
result Empirical results show the algorithm's effectiveness for analyzing long-term multivariate data.
This paper proposes MMD-SVR to improve SVR's margin distribution for better generalization.
problem Improving SVR's generalization performance by maximizing the margin distribution of the whole dataset.
method Introducing MMD-SVR with coupled constraints to convert a non-convex optimization problem into a convex one.
result MMD-SVR significantly improves prediction accuracy and generalization compared to classic SVR.
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.
Machine learning predicts signaling peptides from protein star graphs.
problem Predicting signaling activity of proteins from molecular structure.
method Protein star graphs, S2SNet topological indices, Machine Learning (SVM-RFE, Laplacian kernel).
result Best model predicts 98.0% signaling pathways with AUROC 0.961.
PET-TURTLE improves clustering accuracy for imbalanced data.
problem Imbalanced data causes clustering errors.
method Generalizes cost function and introduces sparse logits.
result PET-TURTLE enhances overall clustering accuracy for imbalanced data.
Optimizes memory and computation in wearable systems.
problem Minimizing resource usage in real-time classification for wearable systems.
method Hierarchical SVM classifier structure and memory optimization techniques.
result Up to 56% reduction in memory storage for activity recognition.
Tuning SVM and boosting models using optimization algorithms.
problem Tuning parameters for SVM and boosting models across various datasets.
method Used grid search to identify parameter ranges and optimization algorithms to select models.
result Optimization algorithms outperformed grid search in selecting well-performing models.
We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature. We investigate the simplest type of LSVMs called Local Linear Support Vector Machines (LLSVMs). For…
Paper explains distance-based classifiers using neural network structures.
problem Making distance-based classifiers explainable.
method Uncovering latent neural network structures in distance-based classifiers.
result Novel explanation approach outperforms baselines.
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constit…
Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales efficiently to very large data sets. Instead of solving the whole training s…
New neural network approach for projection-free optimization.
problem Feasibility constraints in optimization problems.
method Designing projection-free convex optimization algorithms as Frank-Wolfe Networks.
result LSTM-learned optimizers outperform hand-designed and unconstrained optimizers.
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.
Paper proposes SVM-based methods for inferring interaction networks.
problem Modeling interaction between variables in time series and high dimensions.
method Two approaches: neighborhood SVM and restricted Bayesian network for time series.
result Efficiency demonstrated through simulations with linear and nonlinear data.