Add to ORKGKernel Logistic PLS (KL-PLS), a new tool for classification with performances similar to the most powerful statistical methods is described in this paper. KL-PLS is based on the principles of PLS generalized regression and learningvia kernel. The successions of simple regressions, simple logistic regression and multiple logistic regressions on a small number of uncorrelated variables that are computed within KL-PLS algorithm are convenient for the management of very high dimensional data. The algorithm was applied to a variety of benchmark data sets for classification and in all cases, KL-PLS demonstrates its competitivenesswith other state-of-art classification method. Furthermore, leaning on statistical tests related to the logistic regre...
Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recogni...
The generalised linear model (GLM) is the standard approach in classical statistics for regression t...
Logistic regression is often used to solve linear binary classification problems such as machine vis...
Kernel Logistic PLS (KL-PLS), a new tool for classification with performances similar to the most ...
International audienceKernel logistic PLS" (KL-PLS) is a new tool for supervised nonlinear dimension...
The support vector machine (SVM) is known for its good performance in two-class classification, but ...
The support vector machine (SVM) is known for its good performance in binary classification, but its...
The classification of observations is an important constituent of statistics and machine learning, e...
Binary classification is a core data mining task. For large datasets or real-time applications, desi...
Binary classification is a core data mining task. For large datasets or real-time applications, desi...
The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining ...
The goal of the first part of the thesis is to define concepts allowing developing efficient classif...
Third post of our series on classification from scratch, following the previous post introducing smo...
Logistic Regression is a well known classification method in the field of statistical learning. Rece...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recogni...
The generalised linear model (GLM) is the standard approach in classical statistics for regression t...
Logistic regression is often used to solve linear binary classification problems such as machine vis...
Kernel Logistic PLS (KL-PLS), a new tool for classification with performances similar to the most ...
International audienceKernel logistic PLS" (KL-PLS) is a new tool for supervised nonlinear dimension...
The support vector machine (SVM) is known for its good performance in two-class classification, but ...
The support vector machine (SVM) is known for its good performance in binary classification, but its...
The classification of observations is an important constituent of statistics and machine learning, e...
Binary classification is a core data mining task. For large datasets or real-time applications, desi...
Binary classification is a core data mining task. For large datasets or real-time applications, desi...
The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining ...
The goal of the first part of the thesis is to define concepts allowing developing efficient classif...
Third post of our series on classification from scratch, following the previous post introducing smo...
Logistic Regression is a well known classification method in the field of statistical learning. Rece...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
Kernel logistic regression (KLR) is the kernel learning method best suited to binary pattern recogni...
The generalised linear model (GLM) is the standard approach in classical statistics for regression t...
Logistic regression is often used to solve linear binary classification problems such as machine vis...