Results 151 to 160 of about 210,501 (299)
Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization
, 2013 Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many ...
Liu J(刘霁) +3 more
core
Metric learning for kernel ridge regression: assessment of molecular similarity
Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in
Fabregat, Raimon +4 more
core +1 more source
Metabolic and Microvascular Risk Factors Associated With Brain Health in Type 1 Diabetes
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT We examined relationships between metabolic factors, microvascular complications, and brain health in adults with type 1 diabetes. Fifty‐one adults were assessed for metabolic risk factors, microvascular complications, and cognitive function, with a subset completing brain MRI.
Jihyun Park +7 more
wiley +1 more source
Semi-supervised distance metric learning
, 2006 Many machine learning and pattern recognition algorithms rely on a distance metric. Instead of choosing a metric manually, a more promising approach is to learn the metric from data automatically.
Chang, Hong
core
Diance yu yibiaoThe converter valve is the core equipment of DC transmission projects, and its value accounts for about 22%-25% of the total price of converter station equipment. Its operating status directly affects the reliability of the DC transmission system.
WEI Yun +5 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller +14 more
wiley +1 more source
Bounded-Distortion Metric Learning
, 2016 Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical ...
Jianping Shi +4 more
core
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe exponential growth of remote sensing image data and the high cost of manual annotation have led to insufficient labeled data, limiting classification performance.
Pingping Liu +4 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Facioscapulohumeral muscular dystrophy (FSHD) is one of the most debilitating and common muscular dystrophies. Despite its severity, no approved therapy exists for FSHD patients. However, several therapeutic candidates are currently under development, and some have recently entered clinical trials, marking the need for reliable ...
Mustafa Bilal Bayazit +11 more
wiley +1 more source
$K-$means with learned metrics
CoRRWe study the Fréchet $k-$means of a metric measure space when both the measure and the distance are unknown and have to be estimated. We prove a general result that states that the $k-$means are continuous with respect to the measured Gromov-Hausdorff topology. In this situation, we also prove a stability result for the Voronoi clusters they determine.
Pablo Groisman +3 more
openaire +2 more sources