Results 31 to 40 of about 368,237 (276)

Checkerboard Dropout: A Structured Dropout With Checkerboard Pattern for Convolutional Neural Networks

IEEE Access, 2022
Dropout is adopted in many state-of-the-art Deep Neural Networks (DNNs) to ease the overfitting problem by randomly removing features from feature maps.
Khanh-Binh Nguyen, Jaehyuk Choi, Joon-Sung Yang   +2 more
doaj   +1 more source

Instance Reduction for Avoiding Overfitting in Decision Trees

Journal of Intelligent Systems, 2021
Decision trees learning is one of the most practical classification methods in machine learning, which is used for approximating discrete-valued target functions.
Amro Asma’, Al-Akhras Mousa, Hindi Khalil El, Habib Mohamed, Shawar Bayan Abu   +4 more
doaj   +1 more source

A Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks

Applied Computer Systems, 2020
Deconvolutional neural networks are a very accurate tool for semantic image segmentation. Segmenting curvilinear meandering regions is a typical task in computer vision applied to navigational, civil engineering, and defence problems.
Romanuke Vadim
doaj   +1 more source

Methods for Developing a Process Design Space Using Retrospective Data

Pharmaceutics, 2023
Prospectively planned designs of experiments (DoEs) offer a valuable approach to preventing collinearity issues that can result in statistical confusion, leading to misinterpretation and reducing the predictability of statistical models.
Miquel Romero-Obon, Pilar Pérez-Lozano, Khadija Rouaz-El-Hajoui, Marc Suñé-Pou, Anna Nardi-Ricart, Josep M. Suñé-Negre, Encarna García-Montoya   +6 more
doaj   +1 more source

A Shallow Convolutional Neural Network for Apple Classification

IEEE Access, 2020
In the automatic apple sorting task, it is necessary to automatically classify certain apple species. A shallow convolutional neural network (CNN) architecture is proposed for this purpose.
Jinquan Li, Shanshan Xie, Zhe Chen, Hongwen Liu, Jia Kang, Zixuan Fan, Wenjie Li   +6 more
doaj   +1 more source

The Fairness Stitch: A Novel Approach for Neural Network Debiasing

Acta Informatica Pragensia
The pursuit of fairness in machine learning models has become increasingly crucial across various applications, including bank loan approval and face detection.
Modar Sulaiman, Kallol Roy
doaj   +1 more source

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

, 2018
Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker ...
Fredrikson, Matt, Giacomelli, Irene, Jha, Somesh, Yeom, Samuel   +3 more
core   +1 more source

Improving Panic Disorder Classification Using SMOTE and Random Forest

Journal of Applied Informatics and Computing
Panic disorder is a serious anxiety disorder that can significantly impact an individual's mental health. If left undetected, this disorder can disrupt daily life, social relationships, and overall quality of life.
Dini Nurmalasari, Heri R Yuliantoro, Dini Hidayatul Qudsi   +2 more
doaj   +1 more source

Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization

, 2018
Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems that accounts for the similarity ...
Heifets, Abraham, Wallach, Izhar
core   +3 more sources

Feature Learning Viewpoint of Adaboost and a New Algorithm

IEEE Access, 2019
The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomenon is a very fascinating fundamental theoretical problem.
Fei Wang, Zhongheng Li, Fang He, Rong Wang, Weizhong Yu, Feiping Nie   +5 more
doaj   +1 more source