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.
OKSVM optimizes RBF kernel hyperparameter for SVMs, improving classification performance.
Support Vector Machines (SVMs) are powerful learners that have led to state-of-the-art results in various computer vision problems. SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency. This paper introduces a n…
Paper shows SVMs can interpolate data in various settings.
This work simplifies SVM parameter selection using S&S ratio.
This paper uses MIO to select features for kernel SVM classification.
Adaptive caching strategy improves SVM training efficiency.
A quantum-inspired classical algorithm speeds up LS-SVM classification.
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…
m-arcsinh improves SVM and MLP reliability and speed in scikit-learn.
Extends linear classification framework to nonlinear SVM-based ranking problems.
End-to-end graph SVM with graph convolutions and RKHS.
In nonparametric classification and regression problems, regularized kernel methods, in particular support vector machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods (simply called SVMs here) can be seen as regularized M-estimators for a parameter …
Paper introduces MKL--SVM for SVM with loss.
Quantum LS-SVM simplifies matrix inversion for faster machine learning.
The paper improves SVM learning rates for anisotropic Gaussian kernels.
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some loss functions such as the truncated quadratic loss and quadratic loss, SVM with Gaussian kernel can reach the almost optimal learning rate, p…
SVM used for estimating treatment effects without confounding.
In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in th…
Analyzes SVM classifier behavior with different parameters and data types.
This paper presents a kernel-based discriminative learning framework on probability measures. Rather than relying on large collections of vectorial training examples, our framework learns using a collection of probability distributions that have been constructed to meaningfully represent training data. By representing …
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…
Proposes a new kernel technique for tensor data in SVM.
SFM uses linear models in extended feature spaces to avoid kernel issues.
A scalable ROC-SVM variant reduces training time for imbalanced binary classification.
This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data and their number grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS…
It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.
A method for interpreting SVMs using polynomial kernels, revealing model complexity.
Kernel methods linked to feature subspaces and maximal correlation kernels.
The paper improves SVM and localized SVM stability under triple perturbations.
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…
Support vector machines (SVMs) are special kernel based methods and belong to the most successful learning methods since more than a decade. SVMs can informally be described as a kind of regularized M-estimators for functions and have demonstrated their usefulness in many complicated real-life problems. During the last…
Dual SVM training with budget constraint for faster accuracy.
Support Vector Machine (SVM) is powerful classification technique based on the idea of structural risk minimization. Use of kernel function enables curse of dimensionality to be addressed. However, proper kernel function for certain problem is dependent on specific dataset and as such there is no good method on choice …
Localized SVMs maintain SVM's consistency properties for large datasets.
This paper presents a unified framework to tackle estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The use of SVMs in estimation problems has been traditionally limited to its mere use as a black-box model. Noting such limitations in the literature, we take advantage of sever…
The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning nature of SVMs. Based on the insight that perturbing the SVM kernel may help in diversifying SVMs, we propose two kernel perturbation based b…
Discriminative model identifies readers and assesses comprehension from eye movements.
T-SVM improves learning in spiking neurons by maximizing dynamical margin.
Efficiently merges multiple points to speed up BSGD SVM training.
New solver for MKL-SVM with 0/1 loss function.
A new method optimizes kernels for GANs and SVMs using mean-field theory.
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…
Algorithm improves SVM classification in non-Euclidean spaces.
We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resulting optimization problems are equivalent, in the following sense. Given any instance of an $\ell…
Generative models of eye gaze help identify viewers from images.
Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods. But for optimal predictive performance, time-consuming parameter tuning is crucial, which impedes application. To tackle this problem, the classic model selection procedure based on grid-search and cross-validation was …
Novel defense algorithm improves SVMs against data poisoning attacks.
We introduce the anti-profile Support Vector Machine (apSVM) as a novel algorithm to address the anomaly classification problem, an extension of anomaly detection where the goal is to distinguish data samples from a number of anomalous and heterogeneous classes based on their pattern of deviation from a normal stable c…