نتایج جستجو برای: logic regression

تعداد نتایج: 465700  

2011
Yoonhee Kim Qing Li Cheryl D Cropp Heejong Sung Juanliang Cai Claire L Simpson Brian Perry Abhijit Dasgupta James D Malley Alexander F Wilson Joan E Bailey-Wilson

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Works...

Journal: :international journal of information, security and systems management 2014
arash sharafi masouleh nasim dadgar

quality function development (qfd) is a planning tools used to fulfill customer expectation and qfd is a systematic process to translating customer requirement (whats) into technical description (hows). qfd aims to maximize customer satisfactions related to enterprise satisfaction. the inherent fuzziness of relationships in qfd modeling justifies the use of fuzzy regression for estimating the r...

ژورنال: روانشناسی معاصر 2018

Objectives This study examined the role of achievement goals in moderating the relationship between sex and academic self-handicapping.  Methods In our correlational research, 320 university students (154 men and 166 women) were selected by multistage cluster random sampling method. They completed the Academic Self-Handicapping Scale (ASHS) and the Achievement Goal Questionnaire-Revised (AGQ-R...

2014
Seyed Mehran Kazemi David Buchman Kristian Kersting Sriraam Natarajan David Poole

Relational logistic regression (RLR) was presented at the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR-2014). RLR is the directed analogue of Markov logic networks. Whereas Markov logic networks define distributions in terms of weighted formulae, RLR defines conditional probabilities in terms of weighted formulae. They agree for the supervised learni...

Journal: :PLoS Computational Biology 2009
Debopriya Das Matteo Pellegrini Joe W. Gray

Introduction Importance of cis-Regulatory Elements The rapidly emerging field of systems biology is helping us to understand the molecular determinants of phenotype on a genomic scale [1]. Cis-regulatory elements are major sequence-based determinants of biological processes in cells and tissues [2]. For instance, during transcriptional regulation, transcription factors (TFs) bind to very specif...

Arash Sharafi Masouleh Nasim Dadgar

Quality function development (QFD) is a planning tools used to fulfill customer expectation and QFD is a systematic process to translating customer requirement (WHATs) into technical description (HOWs). QFD aims to maximize customer satisfactions related to enterprise satisfaction. The inherent fuzziness of relationships in QFD modeling justifies the use of fuzzy regression for estimating the r...

2004
Jongchan Lee Seung-Jae Yoo Dong Chun Lee

In this study, we propose a novel mobile tracking scheme which utilizes the fuzzy-based decision making with the consideration of the information such as previous location, moving direction and distance to the base station as well as received signal strength, thereby resulting to the estimation performance even much better than the previous schemes. Our scheme divides a cell into many blocks ba...

1991
Pierre Flener Yves Deville

This chapter gives a brief overview of our framework for stepwise synthesis of logic programs from examples and properties. Directives are extracted for the development of a particular synthesis mechanism whose steps are guided by a divide-and-conquer schema. It features deductive and inductive reasoning. Examples and properties are presented to it in a non-incremental fashion. The objectives a...

2017
Matthias W. Lorenz Negin Ashtiani Abdi Frank Scheckenbach Anja Pflug Alpaslan Bülbül Alberico L. Catapano Stefan Agewall Marat Ezhov Michiel L. Bots Stefan Kiechl Andreas Orth Giuseppe D. Norata Jean Philippe Empana Hung-Ju Lin Stela McLachlan Lena Bokemark Kimmo Ronkainen Mauro Amato Ulf Schminke Sathanur R. Srinivasan Lars Lind Akihiko Kato Chrystosomos Dimitriadis Tadeusz Przewlocki Shuhei Okazaki C. D. A. Stehouwer Tatjana Lazarevic Peter Willeit David N. Yanez Helmuth Steinmetz Dirk Sander Holger Poppert Moise Desvarieux M. Arfan Ikram Sebastjan Bevc Daniel Staub Cesare R. Sirtori Bernhard Iglseder Gunnar Engström Giovanni Tripepi Oscar Beloqui Moo-Sik Lee Alfonsa Friera Wuxiang Xie Liliana Grigore Matthieu Plichart Ta-Chen Su Christine Robertson Caroline Schmidt Tomi-Pekka Tuomainen Fabrizio Veglia Henry Völzke Giel Nijpels Aleksandar Jovanovic Johann Willeit Ralph L. Sacco Oscar H. Franco Radovan Hojs Heiko Uthoff Bo Hedblad Hyun Woong Park Carmen Z. Suarez Dong Zhao Pierre Ducimetiere Kuo-Liong Chien Jackie F. Price Göran Bergström Jussi Kauhanen Elena Tremoli Marcus Dörr Gerald Berenson Aikaterini Papagianni Anna Kablak-Ziembicka Kazuo Kitagawa Jaqueline M. Dekker Radojica Stolic Joseph F. Polak Matthias Sitzer Horst Bickel Tatjana Rundek Albert Hofman Robert Ekart Beat Frauchiger Samuela Castelnuovo Maria Rosvall Carmine Zoccali Manuel F. Landecho Jang-Ho Bae Rafael Gabriel Jing Liu Damiano Baldassarre Maryam Kavousi

BACKGROUND For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-au...

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