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In this paper we introduce a Lasso-based method for learning sparse logistic regression models with logical features. Technically, the method is implemented as ...
We focus on logistic regression with categorical variables and propose a method for learning dependencies that are ex- pressed as general Boolean formulas. The ...
We focus on logistic regression with categorical variables and propose a method for learning dependencies that are expressed as general Boolean formulas. The ...
The sparse logistic regression is a type of logistic regression model which embeds feature selection in classification by adding overall and per-dimension scale ...
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What is logistic regression with sparse features?
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We focus on logistic regression with categorical variables and propose a method for learning dependencies that are ex- pressed as general Boolean formulas. The ...
Nov 13, 2014 · I am currently modeling some data using a binary logistic regression. The dependent variable has a good number of positive cases and ...
Missing: Logical | Show results with:Logical
Sep 29, 2017 · Logistic regression is a statistical model where the target variable is a binary variable. Logistic regression can be generalised to another ...
Missing: Logical | Show results with:Logical
We focus on logistic regression with categorical variables and propose a method for learning dependencies that are expressed as general Boolean formulas. The ...
Apr 14, 2014 · Architecture: Logistic regression is a simple linear model. It takes the input features, multiplies them by corresponding weights, sums them up, ...
May 20, 2024 · logical features and syntactic dependencies, as well as guidelines for lemmatization, tokenization, and other tasks. UD defines content ...