Multinomial logistic regression

Results: 161



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KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model Kamal Sarkar Department of Computer Science and Engineering Jadavpur University, Kolkata, India

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Source URL: ceur-ws.org

- Date: 2016-11-18 14:11:21
    2Statistics / Regression analysis / Logistic regression / Logit / Odds ratio / Probit model / Statistical classification / Dependent and independent variables / Multinomial logistic regression / Dummy variable

    Some Issues in Using PROC LOGISTIC for Binary Logistic Regression by David C. Schlotzhauer Contents

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    Source URL: www.ats.ucla.edu

    Language: English - Date: 2016-08-17 18:18:26
    3Statistics / Regression analysis / Linear regression / Probit / Ordinary least squares / Dependent and independent variables / Errors and residuals / General linear model / Econometrics / Dummy variable / Multinomial logistic regression

    Let’s Put Garbage—Can Regressions and Garbage—Can Probits Where They Belong Christopher H. Achen Department of Politics Princeton University Princeton, NJ 08544

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    Source URL: www.columbia.edu

    Language: English - Date: 2005-02-22 11:15:17
    4Statistical natural language processing / Machine learning / Statistics / Artificial intelligence / Topic model / Latent Dirichlet allocation / Gibbs sampling / Multinomial logistic regression / Dynamic topic model / Dirichlet-multinomial distribution

    FP7-ICT Strategic Targeted Research Project (STREP) TrendMiner (NoLarge-scale, Cross-lingual Trend Mining and Summarisation of Real-time Media Streams D3.3.1 Tools for mining non-stationary data - v2 Clustering

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    Source URL: www.trendminer-project.eu

    Language: English - Date: 2014-05-02 17:17:44
    5Regression analysis / Statistics / Linear regression / Multicollinearity / Logistic regression / Ordinary least squares / Nonlinear regression / Residual sum of squares / Data transformation / Multinomial logistic regression / Dummy variable

    January 21, 2011 8:45 WSPCAADA S1793536910000574 Advances in Adaptive Data Analysis Vol. 2, No–462

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    Source URL: rcada.ncu.edu.tw

    Language: English - Date: 2011-02-08 04:22:40
    6Statistical classification / Artificial intelligence / Statistics / Learning / Machine learning / Computational linguistics / Natural language processing / Linear classifier / Naive Bayes classifier / Multinomial logistic regression / Classifier / N-gram

    Large-Scale Language Classification Writing a Detector for 200 Languages on Twitter Jordan Cazamias Chinmayi Dixit

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    Source URL: nlp.stanford.edu

    Language: English - Date: 2015-12-20 04:56:25
    7Regression analysis / Statistical models / Single-equation methods / Choice modelling / Discrete choice / Multinomial logistic regression / Independence of irrelevant alternatives / Logit / Logistic regression / Mode choice / NLOGIT

    On the Relevance of Irrelevant Alternatives ∗ Austin R. Benson Stanford University

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    Source URL: stanford.edu

    Language: English - Date: 2016-02-02 15:50:14
    8Statistical classification / Machine learning / Regression analysis / Econometrics / Support vector machine / Multinomial logistic regression / Supervised learning / Margin classifier / Classifier / Prediction / Linear discriminant analysis / Linear regression

    Classification of Passes in Football Matches using Spatiotemporal Data Michael Horton Joachim Gudmundsson

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    Source URL: www.large-scale-sports-analytics.org

    Language: English - Date: 2016-03-23 17:27:31
    9Statistical models / Regression analysis / Choice modelling / Discrete choice / Mode choice / Mixed logit / Probability distribution / Multinomial logistic regression / Logistic regression / Likelihood function / Mixture model / Gibbs sampling

    Discrete mixtures of GEV models Stephane Hess, Imperial College London & RAND Europe Michel Bierlaire, EPFL John W. Polak, Imperial College London Conference paper STRC 2005

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    Source URL: www.idep.eco.usi.ch

    Language: English - Date: 2012-01-04 14:50:59
    10Consumer theory / Microeconomics / Preference / Choice / Multinomial logistic regression / Affect

    Learning and making novel predictions about others’ preferences Natalia Vélez1, Yuan Chang Leong1, Chelsey Pan, Jamil Zaki, & Hyowon Gweon {nvelez, ycleong, chelspan, jzaki, gweon}@stanford.edu Department of Psycholog

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    Source URL: sll.stanford.edu

    Language: English - Date: 2016-05-13 22:59:21
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