-
RMDL: Random Multimodel Deep Learning for Classification
The continually increasing number of complex datasets each year necessit...
read it
-
Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model. Deep Learning t...
read it
-
Text Classification Algorithms: A Survey
In recent years, there has been an exponential growth in the number of c...
read it
-
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
Diabetic Foot Ulcers (DFU) detection using computerized methods is an em...
read it
-
Face Recognition System
Deep learning is one of the new and important branches in machine learni...
read it
-
Transmitter Classification With Supervised Deep Learning
Hardware imperfections in RF transmitters introduce features that can be...
read it
-
Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare analyze K-Nearest Neighbours and Deep CNNs
Malaria is a deadly disease which claims the lives of hundreds of thousa...
read it
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure 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 solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated 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. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.
READ FULL TEXT
Comments
There are no comments yet.