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.
Paper proposes MS-k-NN for improved convergence rate in k-NN classification.
Paper develops robust -NN algorithm for few samples.
Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting…
Nearest Neighbors (NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we pro…
Improved convergence rate for kNN graph Laplacians with adaptive bandwidth.
PAC-Bayesian bounds improve understanding of K-NN classifier performance.
A new -NN algorithm using surprisal for robust and interpretable nonparametric learning.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
In the -nearest neighborhood model (-NN), we are given a set of points , and we shall answer queries by returning the nearest neighbors of in according to some metric. This concept is crucial in many areas of data analysis and data processing, e.g., computer vision, document retrieval and machi…
Improves consistency of weighted averaging estimators in active learning.
We derive high-probability finite-sample uniform rates of consistency for -NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that -NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the -NN regression rates to establish new …
The -nearest neighbor classification method (-NNC) is one of the simplest nonparametric classification methods. The mutual -NN classification method (MNNC) is a variant of -NNC based on mutual neighborship. We propose another variant of -NNC, the symmetric -NN classification method (SNNC) based …
A new method for learning distance metrics for K-NN classification.
Implementing -NN classification using Gromov--Wasserstein distances
Enhances k-NN accuracy through randomized hyperstructure.
A fast method for LOOCV in k-NN regression reduces computation time.
The -nearest neighbour (-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the -NN classifier. In this thesis we investigate the use of alternative distances for the -NN classifier. We …
Study shows -NN classifier is not universally consistent on but consistent on discrete and specific measure spaces.
Improved OOD detection using label smoothing and k-NN density estimates.
A novel k-NN method estimates conditional mean and variance efficiently.
Multiple classifier systems focus on the combination of classifiers to obtain better performance than a single robust one. These systems unfold three major phases: pool generation, selection and integration. One of the most promising MCS approaches is Dynamic Selection (DS), which relies on finding the most competent c…
Paper studies transfer learning for nonparametric classification, establishing rates and proposing adaptive classifiers.
The -NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct -NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to const…
The paper optimizes k-NN for distributed learning with minimax optimal performance.
We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a r…
We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of -NN prediction, while matching (or improving) the faster prediction time of -NN. The approach consists of aggregating denoised -NN predictors over a small number of distributed subsamples. We show, bot…
The paper introduces methods to solve optimization problems with auxiliary data.
This paper studies the relationship between the classification performed by deep neural networks (DNNs) and the decision of various classical classifiers, namely k-nearest neighbours (k-NN), support vector machines (SVM) and logistic regression (LR), at various layers of the network. This comparison provides us with ne…
Simple k-NN filtering improves model accuracy on noisy labels.
Improves k-NN for monotonic data with robustness against noise.
The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whose distribu…
Proposes LRR and LRLR for improving stock prediction accuracy.
MRI method predicts glioma features, survival, and endothelial proliferation.
Combines k-NN and RVM for improved classification accuracy.
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for trai…
Estimating entropy and mutual information consistently is important for many machine learning applications. The Kozachenko-Leonenko (KL) estimator (Kozachenko & Leonenko, 1987) is a widely used nonparametric estimator for the entropy of multivariate continuous random variables, as well as the basis of the mutual inform…
Adaptive k-NN classifier improves accuracy over fixed k-NN.
Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.
Consistency of k-NN rule proven in sigma-finite dimensional metric spaces.
We propose a direct estimation method for Rényi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets and , respectively with and samples, where is a constant value. Considering the -nearest neighbor (-NN) graph of in the j…
Paper uses K-NN resampling to simulate and evaluate LOB markets.
Paper improves -NN predictive performance with efficient variable selection.
Study shows SNN graph Laplacians converge to k-NN graph Laplacians under large scale asymptotics.
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive …
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average -NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…
Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classi…
Proposes LRVM for better pattern classification of distant test instances.
The paper improves spectral convergence rates for graph Laplacians.