A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Filters
Five Machine Learning Supervised Algorithms for The Analysis and the Prediction of Obesity
2023
Zenodo
Preserved Fulltext
After the training according to the accuracy performance given by each model and the learning curve, the Support Vector Machine was selected and optimized as the final model with 97% of accuracy. ...
Five machine learning classification models including Random Forest, Support Vector Machine, Logistic regression, K-nearest Neighbor, and Ridge Classifier were used for the purpose. ...
Perceptron-MLP, and Support Vector Machines-SVM). ...
doi:10.5281/zenodo.7551284
fatcat:j7cnsctk2vghvnqgmyyunz2tnq
Sparse Bayesian Models: Bankruptcy-Predictors of Choice?
2006
The 2006 IEEE International Joint Conference on Neural Network Proceedings
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2009; you can also visit the original URL.
The file type is application/pdf.
In this paper we show that sparse Bayesian models also known as Relevance Vector Machine (RVMs) are superior to the stateof-the-art machine learning algorithms such as Support Vector Machines (SVMs) therefore ...
This problem has been approached by various methods ranging from statistics to machine learning, however they find a class decision estimate rather than a probabilistic confidence of the class distribution ...
SUPPORT VECTOR MACHINES Support Vector Machines (SVMs) combine two strong ideas: maximum margin classifiers with low capacity and implicit features spaces defined by kernel functions [11] . ...
doi:10.1109/ijcnn.2006.247338
fatcat:fkhojr7kfbhhfpglttjltncwim
Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish
2021
Scientific Research Communications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The predictive efficiency of term weighting schemes is investigated using four supervised learning algorithms (Naive Bayes, support vector machines, the k-nearest neighbor algorithm, and logistic regression ...
Term weighting schemes are critical representation schemes for text documents in the vector space model. ...
Regularized entropy-based term weighting in combination with a random subspace ensemble of support vector machines produced the best predictive performance of all the configurations compared. ...
doi:10.52460/src.2021.004
fatcat:7qwkfgb3ovgojkl2bocc3fjzrm
Assessing naive Bayes and support vector machine performance in sentiment classification on a big data platform
2021
IAES International Journal of Artificial Intelligence (IJ-AI)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
in spark: naive </span><span>B</span><span lang="EN-US">ayes and support vector machine (SVM) are compared to the performance achieved by the standard implementation of these two algorithms on large different ...
In order to evaluate the performance of Apache Spark's machine learning library (MLlib) on a large volume of data, classification accuracies and processing time of two machine learning algorithms implemented ...
Int J Artif Intell ISSN: 2252-8938 Assessing naive bayes and support vector machine performance in… (Redouane Karsi)
993
− Support vector machines: Support vector machines (SVM) is a supervised learning ...
doi:10.11591/ijai.v10.i4.pp990-996
fatcat:kdb4nhup5jejdjohn2hzc3iaru
Prediction of Cardiovascular Diseases based on Machine Learning
2021
ASP Transactions on Internet of Things
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. ...
This paper mainly uses the method of machine learning to predict whether the population is sick or not. ...
Support Vector Machine Support vector machine is based on VC dimension theory and structural risk minimization theory. ...
doi:10.52810/tiot.2021.100035
fatcat:xjnpri5ijfbwjer2omplwrjzsi
Yelp Review Rating Prediction: Machine Learning and Deep Learning Models
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Four machine learning models including Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine are implemented. ...
Data distribution is presented, and one balanced training dataset is built. Two vectorizers are experimented for feature engineering. ...
Decision tree
Linear Support Vector Machine The fourth applied machine learning model is Linear SVM [8] (SGDClassifier, linear classifier with stochastic gradient descent training) with hinge loss ...
arXiv:2012.06690v1
fatcat:6t2zmx2ftnghnpfxz24qgs3ekm
Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset
2016
Southeast Europe Journal of Soft Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this study, a research is conducted with the purpose of discovering further use of the machine learning techniques in healthcare sector. ...
The use of machine learning techniques in healthcare industry has a vital importance and increases rapidly. ...
Basically, the support vector machine is a binary learning machine with some highly elegant properties. ...
doi:10.21533/scjournal.v4i2.97
fatcat:qkchupqo4zchbh4szxxwhvbn3e
AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review
2012
Physics Procedia
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In pattern classification domain, support vector machine has been widely used and shows promising performance. However, it is expensive in terms of time-consuming. ...
It applied boosting algorithm to feature selection and classifier learning for support vector machine classification and it has achieved approved performance through some researcher's pioneering work. ...
Support Vector Machine Support vector machine was developed by Vapnik from the theory of Structural Risk Minimization. ...
doi:10.1016/j.phpro.2012.03.160
fatcat:5lomkwoeavf7pc7bechcxg3qeu
A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program
[chapter]
2020
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). ...
The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program. ...
the subset of NUGENOB dataset. ...
doi:10.1007/978-3-030-50423-6_33
fatcat:bljywdmbv5fb5dzqfkyxofzroa
An Ensemble Learning-based Model for Classification of Insincere Questions
2019
Forum for Information Retrieval Evaluation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this evaluation, we use an ensemble learning method to unite multiple classification models, including logistic regression model, support vector machine, Naive Bayes, decision tree, K-Nearest Neighbor ...
This paper describes the method for the Classification of Insincere Question(CIQ) in FIRE 2019. ...
Acknowledgments This research was supported by the National Social Science Fund of China (No.18BYY125). ...
dblp:conf/fire/HanGSLHKQ19
fatcat:t2oujspylzgxbcr625dnyrh4cu
A Literature Review on Supervised Machine Learning Algorithms and Boosting Process
2017
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
This survey research article prefer two famous supervised machine learning algorithms that is decision trees and support vector machine and presented the recent research works carried out. ...
Yet there is a knowledge data detection process helps the data mining to extract hidden information from the dataset there is a big scope of machine learning algorithms. ...
(v) Weight based structural information helps the support vector machine to quickly train the machine learning algorithm. ...
doi:10.5120/ijca2017914816
fatcat:fwju3kjyknfs3ant3cnczwxnha
Feature Preprocessing with Restricted Boltzmann Machines for Music Similarity Learning
2014
AES International Conference Semantic Audio
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We compare the effects of feature preprocessing on distance function learning using gradient ascent and support vector machines. ...
In this study, we test feature preprocessing with Restricted Boltzmann Machines in combination with established methods for learning distance measures. ...
We model audio similarity using standard machine learning techniques (Support Vector Machines and gradient ascent) for adapting a weighted distance measure to human similarity ratings. ...
dblp:conf/semanticaudio/TranWWG14
fatcat:cvwjhovlnrfepgjykjqnnrdgou
Memory Augmented Matching Networks for Few-Shot Learnings
2019
International Journal of Machine Learning and Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In addition, a weighted prototype is also introduced to improve the overall performance of the proposed model, especially on the complicated benchmark datasets such as mini-ImageNet. ...
Regarding traditional machine learning methods, the more data gathered, the more accurate the intelligent system could perform. ...
These read vectors are the convex combination of the memory matrix and weighting vector, and are defined as: ⃗⃗ ← ∑ ( ) ( ) (7) where, Mt(i) is a cell i th of the memory used with sample t th in the support ...
doi:10.18178/ijmlc.2019.9.6.867
fatcat:27wrqnpmorg6pmexvmmtoknllu
An empirical evaluation of machine learning performance in corporate sales growth prediction
2021
JSIAM Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This study comprehensively examines the ability of machine learning algorithms to integrate analysis of sales growth prediction, with specific focus on random forest, weighted random forest, gradient boosting ...
decision tree, and support vector machine, as well as a least-squares probabilistic classifier. ...
Tokyo Institute of Technology for academic research purposes. ...
doi:10.14495/jsiaml.13.25
fatcat:xujva6zxnjdc3pw2m7kuh4dmji
Distribution-Sensitive Learning on Relevance Vector Machine for Pose-Based Human Gesture Recognition
2015
Procedia Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper proposes a distribution-sensitive prior on a standard statistical learning, i.e., Relevance Vector Machine (RVM), to deal with the imbalanced data problem. ...
Balancing class distribution, i.e., the number of pose samples per class, is one of difficult tasks in machine learning. ...
Relevance vector machine is a popular machine learning technique motivated by the statistical learning theory. ...
doi:10.1016/j.procs.2015.12.160
fatcat:6maan4btzfhixewb6qq5hpmmqm
« Previous
Showing results 1 — 15 out of 302,479 results