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Special Issue on Applied Machine Learning

Grzegorz Dudek
2022 Applied Sciences  
Machine learning (ML) is one of the most exciting fields of computing today [...]  ...  ML covers a wide range of learning algorithms, including classical ones such as linear regression, k-nearest neighbors, decision trees, support vector machines and neural networks, and newly developed  ...  These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Support vector machines are a well-known classifiers due to their superior classification performance.  ... 
doi:10.3390/app12042039 fatcat:ulzgar3shfbmrprd2fpskws7oa

Hybrid learning machines

Ajith Abraham, Emilio Corchado, Juan M. Corchado
2009 Neurocomputing  
In the ninth paper, Ni and Yin describe a hybrid model formed by a mixture of various regressive neural network models, such as temporal self-organizing maps and support vector regressions, for modeling  ...  These techniques range from raw data visualization to clustering and extraction of association rules.  ... 
doi:10.1016/j.neucom.2009.02.017 fatcat:jatayx5ec5h5hd72bjrlw2mhie


Yingjie Tian, Yong Shi, Xiaohui Liu
2012 Technological and Economic Development of Economy  
Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and optimization methods, have become powerful tools for problem solution in machine learning.  ...  Third, we explore other important issues such as lp-norm SVM for feature selection, LOOSVM based on minimizing LOO error bound, probabilistic outputs for SVM, and rule extraction from SVM.  ...  Acknowledgments This work has been partially supported by grants from National Natural Science Foundation of China (No. 70921061, No. 10601064), the CAS/SAFEA International Partnership Program for Creative  ... 
doi:10.3846/20294913.2012.661205 fatcat:vpno6pdnxjcalpsefmutch5qxq


Mausumi Das Nath, St. Xavier's College (Autonomous), Tapalina Bhattasali
2020 Azerbaijan Journal of High Performance Computing  
In this paper, we propose a model of Naïve Bayes and SVM (Support Vector Machine) to detect anomalies and an ensemble approach to solve the weaknesses and to remove the poor detection results  ...  Procedures can be designed to display behavior learned from previous experiences. Machine learning algorithms are used to analyze the abnormal instances in a particular network.  ...  Both regression and classification challenges are solved by using a supervised machine learning approach called Support Vector Machine.  ... 
doi:10.32010/26166127.2020. fatcat:nc3xjcuwsjd37cdtp2xowuvica

A Comprehensive survey of Machine Learning for Intrusion Detection

K. Narayana Rao, Prof. K. Venkata Rao
2019 International Journal of Research in Advent Technology  
This paper presents different methods used in IDS for protecting computers and networks for over a decade. This study analyzes different machine learning methods in IDS.  ...  It also reviews related studies in the period between 2008 and 2017 focusing on single, hybrid, and ensemble classifiers with relevant datasets.  ...  [30] proposed an intelligent hybrid algorithm with Support Vector Machine, Decision Tree, and Simulated Annealing (SA).  ... 
doi:10.32622/ijrat.72201941 fatcat:iwf7uquktjarvbg7h7sccstshi

Machine Learning and Pattern Recognition

2010 Defence Science Journal  
They have used PCA along with support vector regression to estimates the payload and support vector machine for classification of six algorithms.  ...  The score vector-based approach and segment modelling-based approach have been used in a hybrid framework to model the sets of vectors-based representation to obtain a fixedlength pattern representation  ... 
doi:10.14429/dsj.60.502 fatcat:yf2m6wthxjal5dusxkzrpujjly

Various Classifiers Performance Based Machine Learning Methods

2019 International journal of recent technology and engineering  
and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available.  ...  Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program  ...  With the objective of solving these problems, in future, few approaches are introduced for finding a solution to the classification problems.  ... 
doi:10.35940/ijrte.c1069.1183s319 fatcat:dffksetzlzbu5hq5naoueajjue

A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction

S. Sarojini Devi, Y. Radhika
2018 International Journal of Machine Learning and Computing  
networks (ANN), support vector machines (SVM) and decision trees.  ...  Index Terms-Artificial neural networks (ANN), bankruptcy prediction model (BPM), optimization techniques and support vector machines (SVM).  ...  A hybrid switching PSO algorithm and support vector machines for bankruptcy prediction Particle swarm optimization -support vector machines (PSO-SVM) PSO-SVM achieves better performance  ... 
doi:10.18178/ijmlc.2018.8.2.676 fatcat:cmdq5zil2fedderfstiazxq3zm

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Karolina Kudelina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, Galina Demidova
2021 Applied Sciences  
This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources.  ...  As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day.  ...  Machine learning is a powerful tool with a broad set of different algorithms that can be applied for solving many problems.  ... 
doi:10.3390/app11062761 fatcat:ncor7jpujfc4xav3w2b5rt77da

Machine Learning with Big Data

Amit Kumar Tyagi, Rekha G
2019 Social Science Research Network  
Support vector machine (SVM) is a binary classifier which finds linear classifier in higher dimensional feature space to which original data space is mapped.  ...  METHODS OF MACHINE LEARNING AND BIG DATA Supervised learning can be divided into classification and regression.  ...  Major problems that make the machine learning (ML) techniques unsuitable for solving big data classification problems are: (1) An ML technique that is trained on a particular labeled datasets or data domain  ... 
doi:10.2139/ssrn.3356269 fatcat:m7ehu6uh45hitczq5gn4i5rtay

Linear Penalization Support Vector Machines for Feature Selection [chapter]

Jaime Miranda, Ricardo Montoya, Richard Weber
2005 Lecture Notes in Computer Science  
Support Vector Machines have proved to be powerful tools for classification tasks combining the minimization of classification errors and maximizing their generalization capabilities.  ...  We propose a linearly penalized Support Vector Machines (LP-SVM) model where feature selection is performed simultaneously with model construction.  ...  Acknowledgements This project has been funded by the Millennium Science Nucleus on Complex Engineering Systems ( and the project Fondecyt 1040926.  ... 
doi:10.1007/11590316_24 fatcat:ze5srbimr5e7bldgd5c6gznlim

Machine learning in electrocardiogram diagnosis

Abdel-Badeeh M. Salem, Kenneth Revett, El-Sayed A. El-Dahshan
2009 2009 International Multiconference on Computer Science and Information Technology  
The principal reason for this is the expanded set of features that are typically extracted from the ECG time series.  ...  This paper summarizes some of the principle machine learning approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and  ...  SUPPORT VECTOR MACHINE IN ECG CLASSIFICATION Support vector machine technique was firstly proposed for classification and regression tasks by Vapnik [3] .  ... 
doi:10.1109/imcsit.2009.5352689 dblp:conf/imcsit/SalemRE09 fatcat:mrp4rjt53nentaarpqhlrhdhgq

Machine Learning in Big Data

Lidong Wang, Cheryl Ann Alexander
2016 International journal of mathematical, engineering and management sciences  
Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation.  ...  This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data.  ...  Machine learning approaches include decision tree learning, association rule learning, artificial neural networks, support vector machines (SVM), clustering, Bayesian networks, and genetic algorithms,  ... 
doi:10.33889/ijmems.2016.1.2-006 fatcat:eidif7z3afbihflemxwcnxo7xi

Degradation assessment of bearing based on machine learning classification matrix

Satish Kumar, Paras Kumar, Girish Kumar
2021 Eksploatacja i Niezawodnosc  
A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model.  ...  Machine learning classification matrices have been used to train models based on health data and real time feedback.  ...  Principal component analysis and optimized least squares support vector machine based approach was proposed for bearing degradation prediction [11] .  ... 
doi:10.17531/ein.2021.2.20 fatcat:wcrzvh33m5aqroytctuw76xsne

Machine Learning in Chemical Industry

2017 International Journal of Advances in Scientific Research and Engineering  
This is important for tasks such as drug design, industrial process, and manufacturing, with growing applications in the chemical industry.  ...  Machine learning (ML) is the scientific discipline dealing with the ways in which machines learn from experience.  ...  The algorithms work by repeatedly modifying a population of individual solutions. Choosing an appropriate ML method for problem solving in practice is largely dictated by the problem and experience.  ... 
doi:10.7324/ijasre.2017.32524 fatcat:tdr2scxatbaalcid3ut7rakwku
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