Learning vector quantization with training data selection

2006 ◽  
Vol 28 (1) ◽  
pp. 157-162 ◽  
Author(s):  
C.E. Pedreira
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Data Selection ◽  
Training Data Selection

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

scholarly journals Identification of Disease on Leaves Soybean using Modified Otsu and Learning Vector Quantization Neural Networks

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Candra Dewi ◽  
Muhammad Sa’idul Umam ◽  
Imam Cholissodin
Keyword(s):  
Vector Quantization ◽  
Threshold Value ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Blue Color ◽  
Phakopsora Pachyrhizi ◽  
Average Value ◽  
Maximum Value ◽  
Otsu Method ◽  
Soybean Leaf

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

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