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.
New reformulations for multiclass classification problems using optimal transport.
The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
Estimates neural network errors for classification problems.
The cost-sensitive classification problem plays a crucial role in mission-critical machine learning applications, and differs with traditional classification by taking the misclassification costs into consideration. Although being studied extensively in the literature, the fundamental limits of this problem are still n…
Probabilistic learning for binary classification with categorical variables.
Introduces topological deep learning for neural network classification problems.
New NHCAs improve multi-category classification efficiency.
RFSVM uses learned RF similarity for HDLSS classification.
We show how binary classification methods developed to work on i.i.d. data can be used for solving statistical problems that are seemingly unrelated to classification and concern highly-dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addr…
This paper classifies solutions for a specific geometric problem.
Advances few-shot classification by treating it as supervised learning and proposing new training techniques.
Graph-based multi-label classifier extends CULP for multi-label data.
Proposes an angle-based framework for multicategory cost-sensitive classification.
Solves regression problems with CP by converting to classification.
Paper proposes a new efficient transport-based dissimilarity measure for time series classification.
Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification algorithms in various social contexts. Several recent works have focused on fairness with respect to a specific metric, modeled the correspo…
New method visualizes decision boundaries of classification models.
ICE algorithm solves exact 0-1 loss linear classification problem efficiently.
A new kNN imputation method improves classification performance on datasets with missing data.
A novel feature selection method for SVM improves model accuracy and interpretability.
A new method for compressive classification using bridge regression.
We study realizations of Lie algebras by vector fields. A correspondence between classification of transitive local realizations and classification of subalgebras is generalized to the case of regular local realizations. A reasonable classification problem for general realizations is rigorously formulated and an algori…
New approach improves classification guarantees by focusing on direction rather than regression risk.
Extends linear classification framework to nonlinear SVM-based ranking problems.
A good classification method should yield more accurate results than simple heuristics. But there are classification problems, especially high-dimensional ones like the ones based on image/video data, for which simple heuristics can work quite accurately; the structure of the data in such problems is easy to uncover wi…
New method corrects skewed confidence for PbN classification.
Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when…
Machine learning reveals hidden features in knot classification.
Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a …
Neural networks improve functional data classification.
Paper shows similarity learning can lead to strong binary classification performance.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
A new meta-meta classification method tackles few-shot learning tasks.
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, w…
Proposes interpretable time series classification through extracted features.
Study improves learning algorithms for convex polyhedra in Hilbert spaces.
High dimensional data analysis is known to be as a challenging problem. In this article, we give a theoretical analysis of high dimensional classification of Gaussian data which relies on a geometrical analysis of the error measure. It links a problem of classification with a problem of nonparametric regression. We giv…
Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different principles. However, most existing classifiers cast the classification problem as an optimization problem and do not address the issue of statistic…
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probabil…
The classification of class VII surfaces is a very difficult classical problem in complex geometry. It is considered by experts to be the most important gap in the Enriques-Kodaira classification table for complex surfaces. The standard conjecture concerning this problem states that any minimal class VII surface with $…
In this paper we consider three deeply connected classificational problems on four-dimensional manifolds. First we consider and describe locally regular distributions. Second we give a classification of almost complex structures of general position in terms of distributions. Finally we classify nondegenerate Monge-Ampe…
Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in…
This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous app…
New measure of feature influence in classification problems considering feature dependencies.
We consider the problem of classification when inputs correspond to sets of vectors. This setting occurs in many problems such as the classification of pieces of mail containing several pages, of web sites with several sections or of images that have been pre-segmented into smaller regions. We propose generalizations o…
Improved incremental sequence classification with temporal consistency.
In this paper, we present the classification of generalized Wallach spaces and discuss some related problems.