A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This work extends SVM error bounds to weighted SVM and introduces hyperparameter selection methods.
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…
A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article. The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification directio…
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visu…
Novel defense algorithm improves SVMs against data poisoning attacks.
This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a n…
Gradient flow on softmax attention minimizes nuclear norm of weight matrices.
Unified SVM framework tackles multiclass and multilabel classification.
New method uses weighting vectors for efficient boundary and outlier detection.
SVM used for estimating treatment effects without confounding.
OKSVM optimizes RBF kernel hyperparameter for SVMs, improving classification performance.
The paper predicts edge weights in weighted directed networks using metric geometry.
New SVM model balances sparsity and robustness in noisy data.
Paper develops distributed inference for SVM binary classification.
We present a streaming model for large-scale classification (in the context of -SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The -SVM is known to have an equivalent formulation in …
We investigate iterated compositions of weighted sums of Gaussian kernels and provide an interpretation of the construction that shows some similarities with the architectures of deep neural networks. On the theoretical side, we show that these kernels are universal and that SVMs using these kernels are universally con…
Kernel functions in support vector machines (SVM) are needed to assess the similarity of input samples in order to classify these samples, for instance. Besides standard kernels such as Gaussian (i.e., radial basis function, RBF) or polynomial kernels, there are also specific kernels tailored to consider structure in t…
A new term weighting scheme TF-IDFC-RF outperforms others in sentiment analysis.
A new method for high-dimensional classification using Bernstein polynomials.
This work proposes a model averaging method for SVM that avoids redundant covariates and achieves asymptotic optimality.
Paper explains AdaBoost's overfitting resistance from feature learning perspective.
T-SVM improves learning in spiking neurons by maximizing dynamical margin.
Quantum SVM uses fewer features for faster training.
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…
New algorithm reduces ERM problem size while maintaining accuracy.
Distance weighted discrimination (DWD) is a margin-based classifier with an interesting geometric motivation. DWD was originally proposed as a superior alternative to the support vector machine (SVM), however DWD is yet to be popular compared with the SVM. The main reasons are twofold. First, the state-of-the-art algor…
The computational complexity of solving nonlinear support vector machine (SVM) is prohibitive on large-scale data. In particular, this issue becomes very sensitive when the data represents additional difficulties such as highly imbalanced class sizes. Typically, nonlinear kernels produce significantly higher classifica…
People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace. Many existing overlapping clustering methods model each person (or word, or book) as a non-negative weighted combination of "exemplars" who belong solely to one community, with …
A new method detects outliers in dirty data using a leave-out strategy.
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often …
Federated learning optimizes task and resource allocation in balloon networks.
Optimal posterior distributions improve SVM classifiers and parameter selection.
This papers introduces an algorithm for the solution of multiple kernel learning (MKL) problems with elastic-net constraints on the kernel weights. The algorithm compares very favourably in terms of time and space complexity to existing approaches and can be implemented with simple code that does not rely on external l…
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
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.
Localized SVMs maintain SVM's consistency properties for large datasets.
BAEN-SVM improves SVM robustness to noisy data.
New SVM feature selection methods improve 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.
This paper improves SVM prediction uncertainty quantification methods.
Introduces Soft-SVM for binary classification bridging logistic and SVM.
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. …
Paper proposes an ensemble SVM method for efficient VAD.
GADGET SVM uses gossip-based distributed learning for scalable SVMs.
Proposes SVM-based Deep Stacking Network for improved deep learning.
Paper introduces MKL--SVM for SVM with loss.