• Title/Summary/Keyword: logistic regression

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A Logistic Regression for Random Noise Removal in Image Deblurring (영상 디블러링에서의 임의 잡음 제거를 위한 로지스틱 회귀)

  • Lee, Nam-Yong
    • Journal of Korea Multimedia Society
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    • v.20 no.10
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    • pp.1671-1677
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    • 2017
  • In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

A Study on the Power Comparison between Logistic Regression and Offset Poisson Regression for Binary Data

  • Kim, Dae-Youb;Park, Heung-Sun
    • Communications for Statistical Applications and Methods
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    • v.19 no.4
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    • pp.537-546
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    • 2012
  • In this paper, for analyzing binary data, Poisson regression with offset and logistic regression are compared with respect to the power via simulations. Poisson distribution can be used as an approximation of binomial distribution when n is large and p is small; however, we investigate if the same conditions can be held for the power of significant tests between logistic regression and offset poisson regression. The result is that when offset size is large for rare events offset poisson regression has a similar power to logistic regression, but it has an acceptable power even with a moderate prevalence rate. However, with a small offset size (< 10), offset poisson regression should be used with caution for rare events or common events. These results would be good guidelines for users who want to use offset poisson regression models for binary data.

A Study on Life Cycle analysis and prediction of Contents Service in the Wireless Internet (로지스틱 회귀 모형을 이용한 무선인터넷 콘텐츠 서비스의 life cycle 분석 및 예측)

  • Park, Ji-Hong;Jeon, Joon-Hyeon
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.1161-1164
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    • 2005
  • In this paper, we proposed the technique to estimate the life cycle of Internet content services based on the logistic regression model. In this paper, to define parameters of Internet contents estimating life cycle by logistic regression model, we used market size, traffic amount, page view and session-visit number as the parameters of Internet contents estimating life cycle by logistic regression model. In this paper, to compare the performance of our proposed scheme, we estimated life cycle for the download services of bell sound & character contents in mobile network. As a result, using our proposed logistic regression, we were able to estimate exactly the life cycle of the download services of bell sound & character contents.

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Geographically weighted kernel logistic regression for small area proportion estimation

  • Shim, Jooyong;Hwang, Changha
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.2
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    • pp.531-538
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    • 2016
  • In this paper we deal with the small area estimation for the case that the response variables take binary values. The mixed effects models have been extensively studied for the small area estimation, which treats the spatial effects as random effects. However, when the spatial information of each area is given specifically as coordinates it is popular to use the geographically weighted logistic regression to incorporate the spatial information by assuming that the regression parameters vary spatially across areas. In this paper, relaxing the linearity assumption and propose a geographically weighted kernel logistic regression for estimating small area proportions by using basic principle of kernel machine. Numerical studies have been carried out to compare the performance of proposed method with other methods in estimating small area proportion.

Multinomial Kernel Logistic Regression via Bound Optimization Approach

  • Shim, Joo-Yong;Hong, Dug-Hun;Kim, Dal-Ho;Hwang, Chang-Ha
    • Communications for Statistical Applications and Methods
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    • v.14 no.3
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    • pp.507-516
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    • 2007
  • Multinomial logistic regression is probably the most popular representative of probabilistic discriminative classifiers for multiclass classification problems. In this paper, a kernel variant of multinomial logistic regression is proposed by combining a Newton's method with a bound optimization approach. This formulation allows us to apply highly efficient approximation methods that effectively overcomes conceptual and numerical problems of standard multiclass kernel classifiers. We also provide the approximate cross validation (ACV) method for choosing the hyperparameters which affect the performance of the proposed approach. Experimental results are then presented to indicate the performance of the proposed procedure.

Comparison of Classification Models for Sequential Flight Test Results (단계별 비행훈련 성패 예측 모형의 성능 비교 연구)

  • Sohn, So-Young;Cho, Yong-Kwan;Choi, Sung-Ok;Kim, Young-Joun
    • Journal of the Ergonomics Society of Korea
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    • v.21 no.1
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    • pp.1-14
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    • 2002
  • The main purpose of this paper is to present selection criteria for ROK Airforce pilot training candidates in order to save costs involved in sequential pilot training. We use classification models such Decision Tree, Logistic Regression and Neural Network based on aptitude test results of 288 ROK Air Force applicants in 1994-1996. Different models are compared in terms of classification accuracy, ROC and Lift-value. Neural network is evaluated as the best model for each sequential flight test result while Logistic regression model outperforms the rest of them for discriminating the last flight test result. Therefore we suggest a pilot selection criterion based on this logistic regression. Overall. we find that the factors such as Attention Sharing, Speed Tracking, Machine Comprehension and Instrument Reading Ability having significant effects on the flight results. We expect that the use of our criteria can increase the effectiveness of flight resources.

On the Performance Analysis of a Logistic regression based transient signal classifier (Logistic Regression 방법을 이용한 천이 신호 식별 알고리즘 및 성능 분석)

  • Heo, Sun-Cheol;Kim, Jin-Young;Yoon, Byoung-Soo;Nam, Sang-Won;Oh, Won-Cheon
    • Proceedings of the KIEE Conference
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    • 1995.07b
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    • pp.913-915
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    • 1995
  • In this paper, a transient signal classification system using logistic regression and neural networks is presented, where four neural networks such as MLP, MLP-Class, RBF and LVQ are utilized to classify given transient signals, based on the logistic regression method. Also, some test results with experimental transient signal data are provided.

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Preventing the Musculoskeletal Disorders using Association Rule - Based on Result of Multiple Logistic Regression - (연관규칙을 이용한 근골격계 질환 예방 - 다변량 로지스틱 회귀분석의 결과를 기반으로 -)

  • Park, Seung-Hun;Lee, Seog-Hwan
    • Journal of the Korea Safety Management & Science
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    • v.9 no.4
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    • pp.29-38
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    • 2007
  • We adapted association rules of data mining in order to investigate the relation among the factors of musculoskeletal disorders and proposed the method of preventing the musculoskeletal disorders associated with multiple logistic regression in previous study. This multiple logistic regression was difficult to establish the method of preventing musculoskeletal disorders in case factors can't be managed by worker himself, i.e., age, gender, marital status. In order to solve this problem, we devised association rules of factors of musculoskeletal disorders and proposed the interactive method of preventing the musculoskeletal disorders, by applying association rules with the result of multiple logistic regression in previous study. The result of correlation analysis showed that prevention method of one part also prevents musculoskeletal disorders of other parts of body.

Semiparametric kernel logistic regression with longitudinal data

  • Shim, Joo-Yong;Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.2
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    • pp.385-392
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    • 2012
  • Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.

Landslide Susceptibility Analysis and its Verification using Likelihood Ratio, Logistic Regression and Artificial Neural Network Methods: Case study of Yongin, Korea

  • Lee, S.;Ryu, J. H.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.132-134
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    • 2003
  • The likelihood ratio, logistic regression and artificial neural networks methods are applied and verified for analysis of landslide susceptibility in Yongin, Korea using GIS. From a spatial database containing such data as landslide location, topography, soil, forest, geology and land use, the 14 landsliderelated factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression and artificial neural network methods. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the methods. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The v erification results showed satisfactory agreement between the susceptibility map and the exis ting data on landslide locations.

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