A Relevant Score Normalization Method Using Shannon’s Information Measure

2005 ◽  
pp. 311-316 ◽  
Author(s):  
Yu Suzuki ◽  
Kenji Hatano ◽  
Masatoshi Yoshikawa ◽  
Shunsuke Uemura ◽  
Kyoji Kawagoe
Keyword(s):  
Normalization Method ◽  
Information Measure ◽  
Score Normalization

A new score normalization method for speaker verification with virtual impostor model

2004 ◽  
Author(s):  
Woo-Yong Choi ◽  
Jung Gon Kim ◽  
Hyung Soon Kim ◽  
Sung Bum Pan
Keyword(s):  
Speaker Verification ◽  
Normalization Method ◽  
Score Normalization

The Non-Intellectual Norm of Middle School Students’ Mathematics Learning and Its Grade Evaluation Standard: Taking Tianjin as an Example

2021 ◽  
Vol 7 (1) ◽  
pp. 907-922
Author(s):  
Guangming Wang ◽  
Jian Li ◽  
Jingxian Jian
Keyword(s):  
Middle School ◽  
Middle School Students ◽  
Applied Research ◽  
Mathematics Learning ◽  
Middle School Student ◽  
Normalization Method ◽  
School Students ◽  
Evaluation Standard ◽  
Score Normalization

Using the "Middle School Student Mathematics Learning Non-intellectual Questionnaire," a total of 1,400 middle school students in 11 districts and counties of Tianjin were surveyed. According to the data, using the raw score normalization method and the formula “T = 50+10×Z”, the middle school student mathematics learning non-intellectual population and its sub-dimensions norm table were established the corresponding grade evaluation standard was determined. The results of applied research were analyzed for class and individual application cases, and corresponding suggestions were made based on the analysis results.

scholarly journals Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Dwianti Westari ◽  
Keyword(s):  
Classification System ◽  
Health Sector ◽  
Normalization Method ◽  
Training Data ◽  
Z Score ◽  
Classification Result ◽  
Normalization Methods ◽  
Value Range ◽  
Score Normalization

The diabetes classification system is very useful in the health sector. This paper discusses the classification system for diabetes using the K-Means algorithm. The Pima Indian Diabetes (PID) dataset is used to train and evaluate this algorithm. The unbalanced value range in the attributes affects the quality of the classification result, so it is necessary to preprocess the data which is expected to improve the accuracy of the PID dataset classification result. Two types of preprocessing methods are used that are min-max normalization and z-score normalization. These two normalization methods are used and the classification accuracies are compared. Before the data classification process is carried out, the data is divided into training data and test data. The result of the classification test using the K-Means algorithm has shown that the best accuracy lies in the PID dataset which has been normalized using the min-max normalization method, which 79% compared to z-score normalization.

scholarly journals Reducing impacts of systematic errors in the observation data on inversing ecosystem model parameters using different normalization methods

2009 ◽  
Vol 6 (6) ◽  
pp. 10447-10477 ◽  
Author(s):  
L. Zhang ◽  
M. Xu ◽  
M. Huang ◽  
G. Yu
Keyword(s):  
Parameter Estimation ◽  
Ecosystem Model ◽  
Systematic Errors ◽  
Normalization Method ◽  
Observation Data ◽  
Random Errors ◽  
Z Score ◽  
Normalization Methods ◽  
Score Normalization ◽  
The Impact

Abstract. Modeling ecosystem carbon cycle on the regional and global scales is crucial to the prediction of future global atmospheric CO2 concentration and thus global temperature which features large uncertainties due mainly to the limitations in our knowledge and in the climate and ecosystem models. There is a growing body of research on parameter estimation against available carbon measurements to reduce model prediction uncertainty at regional and global scales. However, the systematic errors with the observation data have rarely been investigated in the optimization procedures in previous studies. In this study, we examined the feasibility of reducing the impact of systematic errors on parameter estimation using normalization methods, and evaluated the effectiveness of three normalization methods (i.e. maximum normalization, min-max normalization, and z-score normalization) on inversing key parameters, for example the maximum carboxylation rate (Vcmax,25) at a reference temperature of 25°C, in a process-based ecosystem model for deciduous needle-leaf forests in northern China constrained by the leaf area index (LAI) data. The LAI data used for parameter estimation were composed of the model output LAI (truth) and various designated systematic errors and random errors. We found that the estimation of Vcmax,25 could be severely biased with the composite LAI if no normalization was taken. Compared with the maximum normalization and the min-max normalization methods, the z-score normalization method was the most robust in reducing the impact of systematic errors on parameter estimation. The most probable values of estimated Vcmax,25 inversed by the z-score normalized LAI data were consistent with the true parameter values as in the model inputs though the estimation uncertainty increased with the magnitudes of random errors in the observations. We concluded that the z-score normalization method should be applied to the observed or measured data to improve model parameter estimation, especially when the potential errors in the constraining (observation) datasets are unknown.

scholarly journals Blind score normalization method for PLDA based speaker recognition

2015 ◽  
Author(s):  
Danila Doroshin ◽  
Nikolay Lubimov ◽  
Marina Nastasenko ◽  
Mikhail Kotov
Keyword(s):  
Speaker Recognition ◽  
Normalization Method ◽  
Score Normalization

ON WEIGHTED GENERALIZED RESIDUAL INFORMATION MEASURE

2015 ◽  
Vol 4 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Ashok Kumar ◽  
H C Taneja ◽  
Ashok K Chitkara ◽  
Vikas Kumar
Keyword(s):  
Information Measure

An Enhanced Weather Normalization Method for Identifying Changes in the Building Condition

2021 ◽  
pp. 102354
Author(s):  
Claire Tam ◽  
Zaiyi Liao ◽  
Paul.S.H. Poh
Keyword(s):  
Normalization Method ◽  
Building Condition

Robust Keypoint Normalization Method for Korean Sign Language Translation using Transformer

2020 ◽  
Author(s):  
San Kim ◽  
Chang Jo Kim ◽  
Han-Mu Park ◽  
Yoonyoung Jeong ◽  
Jin Yea Jang ◽  
...  
Keyword(s):  
Sign Language ◽  
Language Translation ◽  
Normalization Method
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