High-Accuracy Wireless Traffic Prediction: A GP-Based Machine Learning Approach

2017 ◽  
Author(s):  
Yue Xu ◽  
Wenjun Xu ◽  
Feng Yin ◽  
Jiaru Lin ◽  
Shuguang Cui
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Traffic Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach

Sadhana ◽  
2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Amir Hosein Azimi ◽  
Saeid Shabanlou ◽  
Fariborz Yosefvand ◽  
Ahmad Rajabi ◽  
Behrouz Yaghoubi
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Scour Depth ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach

2019 ◽  
Vol 47 (7) ◽  
pp. e41-e41 ◽  
Author(s):  
Kunqi Chen ◽  
Zhen Wei ◽  
Qing Zhang ◽  
Xiangyu Wu ◽  
Rong Rong ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Learning Approach ◽  
Machine Learning Approach

Human-like Computer Vision

2021 ◽  
pp. 199-217
Author(s):  
Stephen Muggleton ◽  
Wang-Zhou Dai
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Learning Approach ◽  
Light Sources ◽  
Statistical Machine Learning ◽  
Robust Version ◽  
Machine Learning Approach ◽  
Statistical Approaches ◽  
Noise Robust ◽  
Background Models

Statistical machine learning is widely used in image classification and typically 1) requires many images to achieve high accuracy and 2) does not provide support for reasoning below the level of classification.  By contrast this paper describes an approach called machine learning approach called Logical Vision (LV) which uses a) background knowledge such as light reflection that can itself be learned and used for resolving visual ambiguities, which cannot be easily modeled using statistical approaches, b) a wider class of background models representing classical 2D shapes such as circles and ellipses, c) primitive-level statistical estimators to handle noise in real images, Our results indicate that in real images the new noise-robust version of LV using a single example (ie one-shot LV) converges to an accuracy at least comparable to thirty-shot statistical machine learner on the prediction of hidden light sources.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
Sign in / Sign up

Export Citation Format

Share Document