diff --git a/README.rst b/README.rst index db0ef39..d5fdefa 100644 --- a/README.rst +++ b/README.rst @@ -3,7 +3,7 @@ Text Classification Algorithms: A Survey ################################################ -|DOI| |Best| |medium| |mendeley| |contributions-welcome| |arXiv| |ansicolortags| |contributors| |twitter| +|UniversityCube| |DOI| |Best| |medium| |mendeley| |contributions-welcome| |arXiv| |ansicolortags| |contributors| |twitter| .. figure:: docs/pic/WordArt.png @@ -2620,7 +2620,7 @@ Using git git clone --recursive https://github.com/kk7nc/RMDL.git The primary requirements for this package are Python 3 with Tensorflow. The requirements.txt file -contains a listing of the required Python packages; to install all requirements, run the following: +contains a listing of the required `Python packages `__ to install all requirements, run the following: .. code:: bash @@ -2649,18 +2649,18 @@ success of these deep learning algorithms rely on their capacity to model comple relationships within the data. However, finding suitable structures for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL aims to solve the problem of finding the best deep learning architecture while simultaneously improving the robustness and accuracy through ensembles of multiple deep -learning architectures. In short, RMDL trains multiple models of Deep Neural Network (DNN), -Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines -their results to produce better result of any of those models individually. To create these models, +learning architectures. In short, RMDL trains multiple models of Deep Neural Networks (DNN), +Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combine +their results to produce the better results of any of those models individually. To create these models, each deep learning model has been constructed in a random fashion regarding the number of layers and nodes in their neural network structure. The resulting RDML model can be used in various domains such -as text, video, images, and symbolic. In this Project, we describe RMDL model in depth and show the results +as text, video, images, and symbolism. In this Project, we describe the RMDL model in depth and show the results for image and text classification as well as face recognition. For image classification, we compared our model with some of the available baselines using MNIST and CIFAR-10 datasets. Similarly, we used four -datasets namely, WOS, Reuters, IMDB, and 20newsgroup and compared our results with available baselines. -Web of Science (WOS) has been collected by authors and consists of three sets~(small, medium and large set). +datasets namely, WOS, Reuters, IMDB, and 20newsgroup, and compared our results with available baselines. +Web of Science (WOS) has been collected by authors and consists of three sets~(small, medium, and large sets). Lastly, we used ORL dataset to compare the performance of our approach with other face recognition methods. -These test results show that RDML model consistently outperform standard methods over a broad range of +These test results show that the RDML model consistently outperforms standard methods over a broad range of data types and classification problems. -------------------------------------------- @@ -2689,7 +2689,7 @@ Comparison Text Classification Algorithms | | | | | | * Computationally is very cheap | * Rocchio often misclassifies the type for multimodal class | | | | | -| | * Relevance feedback mechanism (benefits to ranking documents as not relevant) | * This techniques is not very robust | +| | * Relevance feedback mechanism (benefits to ranking documents as not relevant) | * This technique is not very robust | | | | | | | | * linear combination in this algorithm is not good for multi-class datasets | +------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------+ @@ -3039,8 +3039,6 @@ keywords : is authors keyword of the papers This dataset contains 5,736 documents with 11 categories which include 3 parents categories. Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification - - ================================ Text Classification Applications @@ -3206,7 +3204,11 @@ Citations: .. |medium| image:: https://img.shields.io/badge/Medium-Text%20Classification-blueviolet.svg :target: https://medium.com/text-classification-algorithms/text-classification-algorithms-a-survey-a215b7ab7e2d - + +.. |UniversityCube| image:: https://img.shields.io/badge/UniversityCube-Follow%20us%20for%20the%20Latest%20News!-blue.svg + :target: https://www.universitycube.net/news + + .. |mendeley| image:: https://img.shields.io/badge/Mendeley-Add%20to%20Library-critical.svg :target: https://www.mendeley.com/import/?url=https://doi.org/10.3390/info10040150