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Found 14 papers, 6 papers with code
Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification
The predictive performance of the model was compared against five baseline methods as well as a random search, performing either ANN hyperparameter tuning and feature selection.
Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is essential for establishing the beginning of treatment, enriching the outlook, and enhancing the overall well-being of those affected individuals.
Enhancing Hyper-To-Real Space Projections Through Euclidean Norm Meta-Heuristic Optimization
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of them remains a challenge due to the overwhelming amount of candidate solutions to be evaluated, even by using sophisticated algorithms.
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization.
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities.
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks
In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities.
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification
Machine Learning has attracted considerable attention throughout the past decade due to its potential to solve far-reaching tasks, such as image classification, object recognition, anomaly detection, and data forecasting.
Gait Recognition Based on Deep Learning: A Survey
In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately.
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing.
A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks
With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years.
Intestinal Parasites Classification Using Deep Belief Networks
Currently, approximately $4$ billion people are infected by intestinal parasites worldwide.
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines
Machine Learning has been applied in a wide range of tasks throughout the last years, ranging from image classification to autonomous driving and natural language processing.
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values
Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few.
Learnergy: Energy-based Machine Learners
Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures.
