Hard Thresholding Regularised Logistic Regression: Theory and Algorithms

This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments, it is observed that LR learning with gradient ascent technique outperforms general NB classifier. However, under Gaussian Naive Bayes assumption, both classifiers NB and LR perform similar.

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Exact logistic regression: Theory and examples

Statistics in Medicine ◽

10.1002/sim.4780141908 ◽

1995 ◽

Vol 14 (19) ◽

pp. 2143-2160 ◽

Cited By ~ 282

Author(s):

Cyrus R. Mehta ◽

Nitin R. Patel

Keyword(s):

Logistic Regression ◽

Regression Theory

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A Vs-Based Logistic Regression Method for Liquefaction Evaluation

Advances in Civil Engineering ◽

10.1155/2021/5535387 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xiaofei Yao ◽

Lu Liu ◽

Zhihua Wang ◽

Zhifu Shen ◽

Hongmei Gao

Keyword(s):

Logistic Regression ◽

Evaluation Method ◽

Evaluation Methods ◽

Regression Method ◽

Success Rates ◽

Velocity Data ◽

Wide Range ◽

Logistic Regression Method ◽

New Formula ◽

Regression Theory

The current liquefaction evaluation methods mainly focus on the success rate for liquefied sites so that the evaluation result tends to be conservative at different seismic intensities. Therefore, a new formula about liquefaction evaluation by introducing logistic regression theory is proposed to solve the deficiencies of the current evaluation method, which is based on 225 sets of shear wave velocity data reported by Andrus. The reliability of the new formula is verified based on 336 sets of Vs data collected from the Kayen database. The performance of the new formula on liquefaction evaluation is compared with existing liquefaction evaluation methods including the Andrus method and the Chinese code method. Compared with the Andrus method and Chinese code method, the success rates of liquefaction evaluation given by the new formula under different seismic intensities are more balanced between liquefied site and nonliquefied site. The new formula at 50% probability of liquefaction is more adaptable for a wide range of seismic intensities, ground water table, and sand buried depth. In addition, the new formula at different probabilistic levels of liquefaction can be adopted based on the importance of the engineering site in risk analysis.

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Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation

INTELIGENCIA ARTIFICIAL ◽

10.4114/ia.v18i56.1113 ◽

2015 ◽

Vol 18 (56) ◽

pp. 14-30 ◽

Cited By ~ 1

Author(s):

Tapan Kumar Bhowmik

Keyword(s):

Logistic Regression ◽

Experimental Validation ◽

Naive Bayes ◽

Naïve Bayes ◽

Regression Theory

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How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

European Journal of Psychological Assessment ◽

10.1027/1015-5759.23.3.157 ◽

2007 ◽

Vol 23 (3) ◽

pp. 157-165 ◽

Cited By ~ 4

Author(s):

Carmen Hagemeister

Keyword(s):

Logistic Regression ◽

Reaction Time ◽

Power Law ◽

Error Rate ◽

Reaction Times ◽

Practice Effect ◽

Practice Effects ◽

Rate Decrease ◽

Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

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Perceived Effectiveness of Random Testing for Alcohol and Drugs in the Australian Aviation Community

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000031 ◽

2012 ◽

Vol 2 (2) ◽

pp. 72-81

Author(s):

Christina M. Rudin-Brown ◽

Eve Mitsopoulos-Rubens ◽

Michael G. Lenné

Keyword(s):

Logistic Regression ◽

Online Survey ◽

Random Testing ◽

Regression Analyses ◽

Perceived Effectiveness ◽

Student Pilots ◽

Alcohol And Drugs ◽

Positive Attitudes ◽

One Year ◽

Alcohol And Other Drugs

Random testing for alcohol and other drugs (AODs) in individuals who perform safety-sensitive activities as part of their aviation role was introduced in Australia in April 2009. One year later, an online survey (N = 2,226) was conducted to investigate attitudes, behaviors, and knowledge regarding random testing and to gauge perceptions regarding its effectiveness. Private, recreational, and student pilots were less likely than industry personnel to report being aware of the requirement (86.5% versus 97.1%), to have undergone testing (76.5% versus 96.1%), and to know of others who had undergone testing (39.9% versus 84.3%), and they had more positive attitudes toward random testing than industry personnel. However, logistic regression analyses indicated that random testing is more effective at deterring AOD use among industry personnel.

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