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.
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.
Alternative method improves SVM for data classification.
problem SVM's weaknesses in time processing, optimization, multi-class generalization, and unbalanced classes.
method Minimum distance to optimal subspaces containing mapped classes.
result Improved performance with fewer shortcomings.
New scalable multi-class SVM for structured and unstructured data.
problem Classification of structured and unstructured data.
method Bayesian multi-class support vector machine with pseudo-likelihood, variational inference, and inducing point approximation.
result Outperforms competitor methods in training time and accuracy.
A new method reduces both features and samples for sparse SVMs efficiently.
problem Sparse SVMs struggle with large-scale problems due to high computational cost.
method Simultaneously identifies and removes inactive features and samples.
result Significant computational savings without loss of accuracy.
New tree-based SVM methods for multi-class classification.
problem Efficient multi-class classification for large datasets.
method Entropy and generalization error estimation for binary classifiers in tree nodes.
result Proposed methods outperform traditional techniques in speed and 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.
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view…
A new algorithm for faster model selection in twin multi-class SVM.
problem Challenges in effective solution of multi-classification and fast model selection in twin multi-class SVM.
method Sample data set partition strategy, Lagrangian multipliers, piecewise linear update, initialization algorithm, and event-based iteration.
result Comparable classification performance achieved without solving quadratic programming problems.
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.
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…
Paper explores reducing precision in SVM for faster text classification.
problem Efficiency in multi-class text classification training.
method Comparison of SVM trained with reduced precision (16-bit, half) vs original.
result Reduced precision training maintains text classification accuracy.
DCSVM efficiently classifies multi-class data using SVMs with smart partitioning.
problem Multi-class classification using SVMs with high computational cost.
method Divide and conquer approach with smart partitioning of data.
result Reduces the number of classes in each step, making final decisions in logarithmic or linear steps.
Paper proposes a method to locate power grid recordings using ENF sequences.
problem Locating power grid recordings without concurrent power signals.
method Extract ENF sequences from power and audio recordings, develop multi-class SVM model.
result Validation of location authenticity of recordings using ENF sequences.
Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then pr…
Improved Frank-Wolfe for sparse/low-rank problems.
problem Sparse/low-rank optimization problems.
method Primal-Dual Block Frank-Wolfe algorithm.
result Empirically outperforms state-of-the-art methods in classification tasks.
Gradient descent on separable data converges to SVM's max-margin solution.
problem Gradient descent on logistic regression problems with linearly separable data.
method Examination of gradient descent on logistic regression with homogeneous predictors.
result Gradient descent converges to the max-margin solution of SVM.
Survey of multi-class classification methods and their performance.
problem Generalizing binary classification to multi-class problems.
method Recursive control language to describe partitioning strategies, manual and automatic design.
result One-vs.-one is generally the best solution, but adaptive methods can outperform it in some cases.
Deep learning identifies smartphone users from motion sensor data.
problem Smartphone user identification using motion sensor signals.
method Transformed motion signals into images, trained CNN for classification, used SVM for few-shot identification.
result CNN achieved 89.75% multi-class user classification and 96.72% few-shot user identification accuracy.
In this paper, we explore and detail our experiments in a high-dimensionality, multi-class image classification problem often found in the automatic recognition of Sign Languages. Here, our efforts are directed towards comparing the characteristics, advantages and drawbacks of creating and training Support Vector Machi…
Orthogonal coding matrices improve multi-class classification accuracy across various datasets.
problem Improving multi-class classification accuracy using orthogonal coding matrices.
method Optimized orthogonal coding matrices for multi-class classification, compared with other methods.
result Orthogonal coding matrices generally outperform random ECCs and are faster than 1 vs. 1.
New tool detects weak and strong Islamophobic hate speech on social media.
problem Detecting Islamophobic hate speech on social media is challenging due to its varied nature.
method Built a multi-class classifier distinguishing between non-Islamophobic, weak Islamophobic, and strong Islamophobic content using GloVe word embeddings.
result Accuracy of 77.6% and balanced accuracy of 83% on a dataset of 109,488 tweets.
Max-min margin Markov networks improve consistency in structured prediction.
problem Statistical inconsistency in max-margin methods for structured prediction.
method Defining a max-min margin formulation to overcome statistical inconsistency.
result Proves consistency and provides an explicit algorithm with finite sample generalization bounds.
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.
SVM and N-best algorithm classify microbial marker clades from genome sequences.
problem Classifying microbial clades from genome sequences, especially new species.
method Support vector machine (SVM) with N-best algorithm, time series feature extraction, random fragment generation, k-mer size selection.
result Recognition accuracy rates above 28% in top-1 candidate, above 91% in top-10 candidate.
We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for…
Machine learning assesses balance outside clinics, improving therapy efficiency.
problem Lack of feedback from PTs in home balance training.
method Trunk sway data analysis with multi-class SVM.
result ML model achieved 82% accuracy in assessing balance.
This research sets limits on how complex multi-class learning problems can be.
problem Understanding the complexity of multi-class classification problems.
method Established upper bounds on Natarajan dimensions for specific function classes.
result Upper bounds on Natarajan dimensions for multi-class decision trees, random forests, and neural networks.
We analyze adversarial learning using a minimax approach.
problem Analyzing risk bounds in adversarial learning problems.
method Reducing adversarial learning to a minimax statistical learning problem and proving a new risk bound.
result Deriving adversarial risk bounds for SVMs, deep neural networks, and PCA.
New method reduces communication in distributed learning, improving privacy and utility.
problem Distributed learning with minimal communication and privacy protection.
method Non-interactive blind model averaging (BlindAvg) with output perturbation.
result BlindAvg converges to centralized learning with strong L2-regularization and SoftmaxReg for better privacy-utility tradeoff.
No multi-class labels needed for multi-class classification.
problem Multi-class classification without requiring class-specific labels.
method Meta classification learning using pairwise similarity prediction.
result The method learns a multi-class classifier from binary classifier for pairwise similarity.
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. Ho…
Paper develops new algorithms for unsupervised multi-class domain adaptation.
problem Improving performance of machine learning models across different domains without labeled data.
method Introduces MCSD divergence and a new domain adaptation bound, develops adversarial learning objectives, and proposes new algorithms like McDalNets and SymmNets.
result New algorithms improve performance across different domain adaptation settings.
Stochastic mirror descent improves multi-class classification accuracy.
problem Efficient multi-class classification using stochastic optimization.
method Derive risk bounds for stochastic mirror descent algorithm.
result Algorithm provides efficient error bounds for classification.
Develops algorithms for multi-class Neyman-Pearson classification with cost sensitivity.
problem Asymmetric misclassification costs in multi-class classification problems.
method Establishes connection with cost-sensitive learning, proposes two algorithms, extends NP oracle properties.
result Proposes algorithms with theoretical guarantees for multi-class Neyman-Pearson classification.
This paper reviews metrics for evaluating multi-class classification models.
problem Evaluating and comparing multi-class classification models.
method Review and analysis of metrics.
result Promising multi-class metrics are highlighted and their usages are demonstrated.
A new method for generating samples in multi-class scenarios using GANs and classifiers.
problem Generating samples for specific classes in multi-class scenarios.
method Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN) method where the generator is conditional and the classification error is backpropagated.
result The method improves sample generation for specific classes in multi-class scenarios.
SIGTRON improves classification accuracy for imbalanced datasets.
problem Improving classification accuracy for imbalanced datasets.
method SIGTRON is a new sigmoid function with a convex loss function for imbalanced classification.
result SIGTRON models outperform existing methods in balanced and imbalanced datasets.
Paper proposes GEG to enhance fairness in binary and multi-class classification.
problem Fairness in multi-class classification tasks is under-explored.
method Formulates multi-objective problem between effectiveness and fairness constraints, proposes GEG algorithm.
result GEG improves fairness up to 92% and decreases accuracy up to 14%.
We consider a problem of risk estimation for large-margin multi-class classifiers. We propose a novel risk bound for the multi-class classification problem. The bound involves the marginal distribution of the classifier and the Rademacher complexity of the hypothesis class. We prove that our bound is tight in the numbe…
A double sampling strategy reduces multi-class to binary classification for large-scale text data.
problem Efficiently handling large-scale multi-class classification with long-tailed class distributions.
method Double sampling strategy on top of multi-class to binary reduction.
result The proposed approach reduces training and prediction time, memory consumption, and improves predictive performance.
Study improves multi-class domain generalization with a new error bound.
problem Improving multi-class classification performance across multiple domains.
method Kernel-based learning algorithm with a logarithmic generalization error bound.
result Achieved significant performance gains over a pooling strategy empirically.
Combines neural networks and STL for multi-class time-series classification.
problem Lack of interpretability in neural networks for time-series data.
method Proposes a method that uses neural networks to classify time-series data using STL specifications, introducing margin for multi-class classification and STL-based attributes for interpretability.
result Evaluations show improved interpretability and performance compared to state-of-the-art baselines.
A new method identifies class-specific covariates in multi-class prediction tasks.
problem Identifying covariates specifically associated with one or more outcome classes in multi-class prediction tasks.
method Introducing multi forests (MuFs) with multi-way and binary splits to measure class-associated discriminatory ability.
result The multi-class VIM specifically ranks class-associated covariates highly, unlike conventional VIMs.
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.
Develops a multi-class classifier using quantum detection theory.
problem Improving multi-class classification models in machine learning.
method Inspired by quantum detection theory, develops a multi-class classifier.
result Demonstrates improved effectiveness of multi-class classification models.