Related papers: Selecting Quasar Candidates by a SVM Classificatio…

The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a…

Classification is one of the main areas of pattern recognition research, and within it, Support Vector Machine (SVM) is one of the most popular methods outside of field of deep learning -- and a de-facto reference for many Machine Learning…

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate…

Voice activity detection (VAD), used as the front end of speech enhancement, speech and speaker recognition algorithms, determines the overall accuracy and efficiency of the algorithms. Therefore, a VAD with low complexity and high accuracy…

We have made initial studies of the potential of support vector machines (SVM) for providing statistical models of nuclear systematics with demonstrable predictive power. Using SVM regression and classification procedures, we have created…

Context. The identification of bright QSOs is of great importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in currently available photometric…

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 $p$ and their number $n$ grow large at the same rate.…

Support vector machines (SVMs) are widely used machine learning models (e.g., in remote sensing), with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM…

We present catalogs of QSO candidates selected using photometry from GALEX combined with SDSS in the Stripe 82 region and Blanco Cosmology Survey (BCS) near declination -55 degrees. The SDSS region contains ~700 objects with magnitude i <…

Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we…

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem…

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate…

The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice property of SVM is that…

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM)…

When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection…

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular…

This paper explores Uncertainty Quantification (UQ) in SVM predictions, particularly for regression and forecasting tasks. Unlike the Neural Network, the SVM solutions are typically more stable, sparse, optimal and interpretable. However,…

Classification will be an important first step for upcoming surveys that will detect billions of new sources such as LSST and Euclid, as well as DESI, 4MOST and MOONS. The application of traditional methods of model fitting and…

This study investigates quantum computing's role in B-cell epitope prediction using Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC). It highlights the potential of quantum machine learning in bioinformatics,…

In this paper, support vector machine (SVM) performance was assessed utilizing a quantum-inspired complementary metal-oxide semiconductor (CMOS) annealer. The primary focus during performance evaluation was the accuracy rate in binary…