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
Keyword(s):  
Face Recognition ◽  
Learning Model ◽  
Hierarchical Learning ◽  
Model Based ◽  
Aging Face ◽  
Local Patterns

Kearifan Lokal Masyarakat Cibalong dalam Melestarikan Keanekaragaman dan Pola Bersarang Kelelawar Penghuni Gua di Kawasan Karst Cibalong Kabupaten Tasikmalaya Pada Pengembangan Model Pembelajaran Berbasis Kearifan Lokal di SD

2016 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Mohammad Fahmi Nugraha
Keyword(s):  
Environmental Problems ◽  
Learning Model ◽  
Scientific Thinking ◽  
Model Based

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.

scholarly journals DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65091-65100
Author(s):  
Ayyad Maafiri ◽  
Omar Elharrouss ◽  
Saad Rfifi ◽  
Somaya Ali Al-Maadeed ◽  
Khalid Chougdali
Keyword(s):  
Face Recognition ◽  
L1 Norm ◽  
Recognition Model ◽  
Model Based

scholarly journals MODES: model-based optimization on distributed embedded systems

2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Learning Model ◽  
Black Box ◽  
Distributed Embedded Systems ◽  
Data Set ◽  
Individual Model ◽  
Model Based ◽  
Machine Learning Model ◽  
Distributed Machine Learning

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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