[HTML][HTML] Selecting training sets for support vector machines: a review
Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …
plethora of real-life applications. However, they suffer from the important shortcomings of …
Accelerating materials discovery using machine learning
Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …
modern society and technology innovation, the traditional materials research mainly …
[HTML][HTML] Evaluation of a decided sample size in machine learning applications
D Rajput, WJ Wang, CC Chen - BMC bioinformatics, 2023 - Springer
Background An appropriate sample size is essential for obtaining a precise and reliable
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
outcome of a study. In machine learning (ML), studies with inadequate samples suffer from …
Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
Intrusion detection has become essential to network security because of the increasing
connectivity between computers. Several intrusion detection systems have been developed …
connectivity between computers. Several intrusion detection systems have been developed …
Voting Classification‐Based Diabetes Mellitus Prediction Using Hypertuned Machine‐Learning Techniques
Diabetes mellitus is a hyperglycemia‐like chronic condition that is a troublesome disease. It
is estimated that, according to the growing morbidity, by 2040, the world will cross 642 …
is estimated that, according to the growing morbidity, by 2040, the world will cross 642 …
A new intrusion detection system using support vector machines and hierarchical clustering
Whenever an intrusion occurs, the security and value of a computer system is compromised.
Network-based attacks make it difficult for legitimate users to access various network …
Network-based attacks make it difficult for legitimate users to access various network …
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and
regression analysis because of their solid mathematical foundations which convery several …
regression analysis because of their solid mathematical foundations which convery several …
[HTML][HTML] An efficient instance selection algorithm to reconstruct training set for support vector machine
C Liu, W Wang, M Wang, F Lv, M Konan - Knowledge-Based Systems, 2017 - Elsevier
Support vector machine is a classification model which has been widely used in many
nonlinear and high dimensional pattern recognition problems. However, it is inefficient or …
nonlinear and high dimensional pattern recognition problems. However, it is inefficient or …
Evolving data-adaptive support vector machines for binary classification
Support vector machines (SVMs) have been exploited in a plethora of real-life classification
and regression tasks, and are one of the most researched supervised learners. However …
and regression tasks, and are one of the most researched supervised learners. However …
Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction
In this paper, we model the trajectory of sea vessels and provide a service that predicts in
near-real time the position of any given vessel in 4′, 10′, 20′ and 40′ time intervals. We …
near-real time the position of any given vessel in 4′, 10′, 20′ and 40′ time intervals. We …