From 0508685cc1d4fda12b4862028d7e4ef5f9050a37 Mon Sep 17 00:00:00 2001 From: Florian Berding Date: Wed, 24 Apr 2024 12:17:10 +0200 Subject: [PATCH] Final version of 0.3.3 --- README.Rmd | 6 +++--- README.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.Rmd b/README.Rmd index e514821..b4eec21 100644 --- a/README.Rmd +++ b/README.Rmd @@ -229,7 +229,7 @@ applying AI. These are: In order to deal with the problem of imbalanced data sets, the package integrates the *Synthetic Minority Oversampling Technique* into the learning process. Currently, the *Basic Synthetic Minority Oversampling -Technique* (Chawla et al. 2002), *Density-Bases Synthetic Minority +Technique* (Chawla et al. 2002), *Density-Based Synthetic Minority Oversampling Technique* (Bunkhumpornpat, Sinapiromsaran & Lursinsap 2012), and *Adaptive Synthetic Sampling Approach for Imbalanced Learning* (Hem Garcia & Li 2008) are implemented via the *R* package @@ -267,7 +267,7 @@ evaluated with the following measures and concepts: - Cohen's Kappa with squared weights - Fleiss' Kappa for multiple raters without exact estimation -In Addition the some traditional measures from the machine learning +In addition the some traditional measures from the machine learning literature are also available: - Precision @@ -276,7 +276,7 @@ literature are also available: ## Sharing Trained AI -Since the package is based on keras, tensorflow, and the transformer +Since the package is based on torch, tensorflow, and the transformer libraries, every trained AI can be shared with other educators and researchers. The package supports an easy use of pre-trained AI within *R*, but also provides the possibility to export trained AI to other diff --git a/README.md b/README.md index 071435c..92ef90e 100644 --- a/README.md +++ b/README.md @@ -218,7 +218,7 @@ applying AI. These are: In order to deal with the problem of imbalanced data sets, the package integrates the *Synthetic Minority Oversampling Technique* into the learning process. Currently, the *Basic Synthetic Minority Oversampling -Technique* (Chawla et al. 2002), *Density-Bases Synthetic Minority +Technique* (Chawla et al. 2002), *Density-Based Synthetic Minority Oversampling Technique* (Bunkhumpornpat, Sinapiromsaran & Lursinsap 2012), and *Adaptive Synthetic Sampling Approach for Imbalanced Learning* (Hem Garcia & Li 2008) are implemented via the *R* package @@ -256,7 +256,7 @@ evaluated with the following measures and concepts: - Cohen’s Kappa with squared weights - Fleiss’ Kappa for multiple raters without exact estimation -In Addition the some traditional measures from the machine learning +In addition the some traditional measures from the machine learning literature are also available: - Precision @@ -265,7 +265,7 @@ literature are also available: ## Sharing Trained AI -Since the package is based on keras, tensorflow, and the transformer +Since the package is based on torch, tensorflow, and the transformer libraries, every trained AI can be shared with other educators and researchers. The package supports an easy use of pre-trained AI within *R*, but also provides the possibility to export trained AI to other