Results 81 to 90 of about 822,037 (305)
Person Re-Identification Using Hybrid Representation Reinforced by Metric Learning
IEEE Access, 2018 Person Re-Identification (Re-Id) is among the main constituents of an automated visual surveillance system. It aims at finding out true matches of a given query person from a large repository of non-overlapping camera images/videos.
Nazia Perwaiz +2 more
doaj +1 more source
An Efficient Dual Approach to Distance Metric Learning
, 2013 Distance metric learning is of fundamental interest in machine learning because the distance metric employed can significantly affect the performance of many learning methods.
Hengel, Anton van den +4 more
core +1 more source
, 2011 Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is ...
Cuturi, Marco, Avis, David
openaire +2 more sources
Molecular Oncology, EarlyView.We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck +12 more
wiley +1 more source
Convolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data
Plants, 2020 Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images.
Ahmed Afifi +2 more
doaj +1 more source
Max-margin Metric Learning for Speaker Recognition
, 2016 Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition.
Li, Lantian +3 more
core +1 more source
Next‐generation proteomics improves lung cancer risk prediction
Molecular Oncology, EarlyView.This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj +4 more
wiley +1 more source
Motor Imagery Classification for Brain Computer Interface Using Deep Metric Learning
IEEE Access, 2020 Deep metric learning (DML) has achieved state-of-the-art results in several deep learning applications. However, this type of deep learning models has not been tested on the classification of electrical brain waves (EEG) for brain computer interface (BCI)
Haider Alwasiti +2 more
doaj +1 more source
Machine Learning Calabi–Yau Metrics
Fortschritte der Physik, 2020 AbstractWe apply machine learning to the problem of finding numerical Calabi–Yau metrics. Building on Donaldson's algorithm for calculating balanced metrics on Kähler manifolds, we combine conventional curve fitting and machine‐learning techniques to numerically approximate Ricci‐flat metrics. We show that machine learning is able to predict the Calabi–
Anthony Ashmore +2 more
openaire +4 more sources
Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
wiley +1 more source