scholarly journals Visual Information Features and Machine Learning for Wushu Arts Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jing Li ◽  
Guangren Zhou
Keyword(s):  
Machine Learning ◽  
Martial Arts ◽  
Visual Information ◽  
Machine Learning Method ◽  
Important Research ◽  
Complicated Problem ◽  
Interactive Animation ◽  
Important Research Topic ◽  
Representation Method ◽  
Posture Tracking

Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts’ body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method.

Empirical Comparative Study of Boosting and its Relatives

2013 ◽  
Vol 312 ◽  
pp. 667-672
Author(s):  
Fang Jun Wu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Transfer Learning ◽  
Research Topic ◽  
Performance Difference ◽  
Important Research ◽  
Knowledge And Skills ◽  
Statistical Framework ◽  
Important Research Topic ◽  
F Measure

Transfer learning is an important research topic in machine learning and data mining that focuses on utilizing knowledge and skills learned in previous tasks to a novel but related task. This paper contributes to comparison between boosting for transfer learning and boosting. The results, in terms of the accuracy, weighted F-Measure, G-Mean, weighted GMPR, weighted precision and weighted AUC, are rigorously tested using the statistical framework proposed by Janez Demsar. Results show that the performance difference between TrAdaBoost and AdaBoost is less significant.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

A Bibliometric Review on Outcome-Based Learning for Graduate Employability: Mapping the Research Front

2021 ◽  
pp. 002205742110164
Author(s):  
Mohammad Zahir Raihan ◽  
Md. Abul Kalam Azad
Keyword(s):  
Research Trend ◽  
Future Research ◽  
Research Front ◽  
Important Research ◽  
R Software ◽  
Future Research Agenda ◽  
Graduate Employability ◽  
Important Research Topic ◽  
Bibliometric Review ◽  
First Time

The outcome-based learning for graduate employability in higher education has been an important research topic among the policymakers, academicians, and researchers over the years. Yet, no bibliometric review on this topic has been published. This study, for the first time, examines bibliometric analysis on this topic examining current research trend and future research agenda. The bibliometrix package in R software and VOSviewer software are used for visualization and interpretation of results. A content analysis is performed to manually examine the bibliometric results.

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