Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.

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Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.

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2017 nî lūn-bûn

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2017年の論文

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年學術文章

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2017年學術文章

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Identifying Environmental and ...... on and Logic Learning Machine.

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Identifying Environmental and ...... on and Logic Learning Machine.

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Identifying Environmental and ...... on and Logic Learning Machine.

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P1476

Identifying Environmental and ...... ion and Logic Learning Machine

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P2093

Antonella Zambon

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Corrado Dosi

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Enrico Ferrari

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Marco Muselli

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P2860

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

Evaluating switching neural networks through artificial and real gene expression data.

Prediction and interaction in complex disease genetics: experience in type 1 diabetes.

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

A pathways model of problem and pathological gambling.

Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: A systematic review and meta-analysis.

Differential diagnosis of pleural mesothelioma using Logic Learning Machine.

Evaluating the South Oaks Gambling Screen with DSM-IV and DSM-5 criteria: results from a diverse community sample of gamblers

Risk factors for problematic gambling: a critical literature review.

The subtyping of pathological gambling: a comprehensive review.

P2888

s10899-017-9679-1

P304

1121-1137

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scholarly article

P356

10.1007/S10899-017-9679-1

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P433

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P478

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Stefano Parodi

P577

2017-12-01T00:00:00Z

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P6179

1084027835

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P698

28255941

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P921

logistic regression model

machine learning