Results 31 to 40 of about 1,249,172 (345)
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis [PDF]
, 2020 Fisher's linear discriminant analysis is a classical method for classification, yet it is limited to capturing linear features only. Kernel discriminant analysis as an extension is known to successfully alleviate the limitation through a nonlinear feature mapping.
arxiv +1 more source
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
Using giant african pouched rats to detect human tuberculosis: a review
The Pan African Medical Journal, 2015 Despite its characteristically low sensitivity, sputum smear microscopy remains the standard for diagnosing tuberculosis (TB) in resource-poor countries. In an attempt to develop an alternative or adjunct to microscopy, researchers have recently examined
Alan Poling +6 more
doaj +1 more source
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions [PDF]
arXiv, 2023 Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination, which is inherent in the data distribution, and epistemic discrimination, which is due to decisions ...
arxiv
Symmetry and discrimination learning
Acta Psychologica, 1974 Abstract A hypothesis that the difficulties which subjects encounter whilst engaged in a traditional discrimination learning task may be seen as influenced by symmetry of individual stimuli as well as of arrangements of stimuli was examined. The data obtained as well as published data are interpreted as suggesting that the nature of symmetry of an ...
Diane Ellis, Jan B. Deregowski
openaire +2 more sources
Learning Outcomes for Improving Science Entrepreneurship in Higher Education
SAGE Open, 2021 This study’s purpose was to investigate the relationships among learning through experience, learning for stress resistance, learning cognition, learning outcomes, the entrepreneurial mindset, and the discrimination of social information.
Kun-Dang Chen +2 more
doaj +1 more source
Generative-Discriminative Complementary Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 The majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases.
Tongliang Liu +5 more
openaire +5 more sources
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates [PDF]
arXiv, 2021 We propose a simple but effective modification of the discriminators, namely measure-conditional discriminators, as a plug-and-play module for different GANs. By taking the generated distributions as part of input so that the target optimum for the discriminator is stationary, the proposed discriminator is more robust than the vanilla one. A variant of
arxiv
Taking service providers to court: people with learning disabilities and Part III of the Disability Discrimination Act 1995 [PDF]
, 2009 Despite evidence of poor service provision for people with learning disabilities in the UK (e.g. DRC, Code of practice. Rights of access: services to the public, public authority functions, private clubs and premises. London: The Stationery Office, 2006;
Lerpiniere, Jennifer, Stalker, Kirsten
core +1 more source
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
, 2020 As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making.
Babaki, Behrouz +3 more
core +1 more source