Search Results for author:
Found 10 papers, 0 papers with code
Distributed Averaging CNN-ELM for Big Data
This approach also increased the scalability of machine learning by combining scale out and scale up approaches.
Adaptive Convolutional ELM For Concept Drift Handling in Online Stream Data
We propose an Adaptive OS-ELM (AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1) and matrices concatenation ensembles for concept drift adaptability in ensemble level (named ACNNELM-2).
Optimization of Convolutional Neural Network using Microcanonical Annealing Algorithm
The performance of the proposed method is tested using the MNIST and CIFAR-10 datasets.
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification
In this study, we used two datasets: an open data set that is treated as a benchmark; the other dataset is our sleep stages dataset (available for download) to verify the results further.
Metaheuristic Algorithms for Convolution Neural Network
Furthermore, the proposed methods are also compared with the original CNN.
Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8
This study uses remote sensing technology that can provide information about the condition of the earth's surface area, fast, and spatially.
Adaptive Online Sequential ELM for Concept Drift Tackling
In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem.
A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text
In addition, the medical text mining poses more challenges, e. g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug.
Sequence-based Sleep Stage Classification using Conditional Neural Fields
Treating the sleep signals as sequences, this paper compared two powerful unsupervised feature extractors and three sequence-based classifiers regarding accuracy and computational (training and testing) time after 10-folds cross-validation.
Ischemic Stroke Identification Based on EEG and EOG using 1D Convolutional Neural Network and Batch Normalization
Because of the availability, another device that potential to diagnose stroke in Indonesia is EEG.
