Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection

2016 ◽  
Vol 25 (5) ◽  
pp. 2146-2154 ◽  
Author(s):  
Zhifeng Li ◽  
Dihong Gong ◽  
Xuelong Li ◽  
Dacheng Tao
Author(s):  
Mohammad Fahmi Nugraha

The environmental problems at this time, especially the diversity of bat cave dwellers in the karst of Cibalong, Tasikmalaya should be given the special attention by all of the society elements, especially by the educators who must act real and solve the problems to give the view of knowledge to the community and the students in understanding the importance of bats which is considered as a pest and it is associated with mystical things. One of the effort is looking for and implementing  some of learning model based on the local wisdom to change and establish the scientific thinking of the sociaety and the students to analyze the presence of bat in term of the survival of the ecosystem. It is expected that bats and their habitats in Karst of Cibalong, Tasikmalaya can be preserved.


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65091-65100
Author(s):  
Ayyad Maafiri ◽  
Omar Elharrouss ◽  
Saad Rfifi ◽  
Somaya Ali Al-Maadeed ◽  
Khalid Chougdali

2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.


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