Results 51 to 60 of about 1,295,192 (344)

Class imbalance ensemble learning based on the margin theory [PDF]

, 2018
The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance.
Feng, Wei, Huang, Wenjiang, Ren, Jinchang   +2 more
core   +2 more sources

Large-Margin Regularized Softmax Cross-Entropy Loss

IEEE Access, 2019
Softmax cross-entropy loss with L2 regularization is commonly adopted in the machine learning and neural network community. Considering that the traditional softmax cross-entropy loss simply focuses on fitting or classifying the training data accurately ...
Xiaoxu Li, Dongliang Chang, Tao Tian, Jie Cao   +3 more
doaj   +1 more source

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

, 2020
Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE).
Imran, Ali, Manalastas, Marvin, Masood, Usama, Shodamola, Joel   +3 more
core   +1 more source

Ensemble Learning for Free with Evolutionary Algorithms ? [PDF]

, 2007
Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result.
Gagné, Christian, Schoenauer, Marc, Sebag, Michèle, Tomassini, Marco   +3 more
core   +4 more sources

Investigating the effect of sound in horror clip on the cardiac electrophysiology of young adults using wavelet packet decomposition and machine learning classifiers

Biomedical Engineering Advances, 2022
The effect of horror sound on electro-cardiac activity is not yet sufficiently explored despite recommendable research on different genres of music and sound. Detecting the impact of these stimuli is a complex task.
Bikash K. Pradhan, Chinmaya R. Mishra, Maciej Jarzębski, J Sivaraman, Sirsendu S. Ray, Satyapriya Mohanty, Kunal Pal   +6 more
doaj   +1 more source

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

, 2018
Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning.
Arif, Rezoana Bente, Khan, Mohammad Mahmudur Rahman, Oishe, Mahjabin Rahman, Siddique, Md. Abu Bakr   +3 more
core   +1 more source

Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites [PDF]

, 2014
This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns.
Basu, Arindam, Hussain, Shaista, Liu, Shih-Chii   +2 more
core   +2 more sources

Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia

Mathematics
The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. Model
Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis   +3 more
semanticscholar   +1 more source

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

Cancers, 2021
Simple Summary Complete resection of dysplastic and malignant tissue improves overall survival and delays cancer recurrence in oral cancer patients; however, intraoperative surgical margin assessment is limited to visual inspection and palpation, making ...
Elvis Duran-Sierra, Shuna Cheng, Rodrigo Cuenca, Beena Ahmed, Jim Ji, V. Yakovlev, Mathias Martinez, Moustafa Al-Khalil, Hussain Al-Enazi, Y. Cheng, John M. Wright, C. Busso, J. Jo   +12 more
semanticscholar   +1 more source

On Breiman's Dilemma in Neural Networks: Phase Transitions of Margin Dynamics

, 2018
Margin enlargement over training data has been an important strategy since perceptrons in machine learning for the purpose of boosting the robustness of classifiers toward a good generalization ability.
Huang, Yifei, Yao, Yuan, Zhu, Weizhi
core   +1 more source