Results 251 to 260 of about 283,141 (292)

A Comparison of Machine Learning Classifiers

Advanced Materials Research, 2011
A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and
Phani Srikanth, Amarjot Singh, Devinder Kumar, Aditya Nagrare, Vivek Angoth   +4 more
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Learning to classify in large committee machines

Physical Review E, 1994
The ability of a two-layer neural network to learn a specific non-linearly-separable classification task, the proximity problem, is investigated using a statistical mechanics approach. Both the tree and fully connected architectures are investigated in the limit where the number K of hidden units is large, but still much smaller than the number N of ...
, O'Kane, , Winther
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A Machine Learning Approach to Classify Network Traffic

2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2021
There is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic
Nilesh Kumar Jadav, Nitul Dutta, Hiren Kumar Deva Sarma, Emil Pricop, Sudeep Tanwar   +4 more
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Applying Machine Learning Classifiers in Argumentation Context

2020
Group decision making is an area that has been studied over the years. Group Decision Support Systems emerged with the aim of supporting decision makers in group decision-making processes. In order to properly support decision-makers these days, it is essential that GDSS provide mechanisms to properly support decision-makers. The application of Machine
Luís Conceição, João Carneiro 0001, Goreti Marreiros, Paulo Novais   +3 more
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ROC analysis of classifiers in machine learning: A survey [PDF]

Intelligent Data Analysis, 2013
The use of ROC (Receiver Operating Characteristics) analysis as a tool for evaluating the performance of classification models in machine learning has been increasing in the last decade. Among the most notable advances in this area are the extension of two-class ROC analysis to the multi-class case as well as the employment of ROC analysis in cost ...
Matjaz Majnik, Zoran Bosnic
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Machine learning classifiers using stochastic logic

2016 IEEE 34th International Conference on Computer Design (ICCD), 2016
This paper presents novel architectures for machine learning based classifiers using stochastic logic. Two types of classifier architectures are presented. These include: linear support vector machine (SVM) and artificial neural network (ANN). Stochastic computing systems require fewer logic gates and are inherently fault-tolerant.
Yin Liu 0002, Hariharasudhan Venkataraman, Zisheng Zhang, Keshab K. Parhi   +3 more
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The Extreme Learning Machine Algorithm for Classifying Fingerprints

2020 39th International Conference of the Chilean Computer Science Society (SCCC), 2020
Fingerprint recognition is the most employed bio-metric method for identification and verification purposes. Fingerprint images are classified into five categories according to the morphology of their ridges, which decreases the database penetration rate on an identification scheme. The classification procedure mainly starts with the feature extraction
David Zabala-Blanco, Marco Mora, Ruber Hernández-García, Ricardo J. Barrientos   +3 more
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Machine learning approach for classifying histone modifications

2009 IEEE International Workshop on Genomic Signal Processing and Statistics, 2009
Post-translational modifications pegged on to the N-terminal tails of the nucleosomes core histone proteins determine the transcriptional activity of that chromosomal region leading to the histone code hypothesis. We rely on recently produced experimental data on genome-wide maps of chromatin state to derive computational models delineating the hidden ...
Aparna Gorthi, Ravi Jain, Nevenka Dimitrova   +2 more
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Classifying Exoplanets with Machine Learning

More than 4200 exoplanets have been detected and their diversity is remarkable, ranging from very small rocky planets, topuffed gas giants. Several of their types are unknown in our Solar System, hence new classes have been defined to understand thisdiversity and the similarities within each group, such as their formation mechanism or core composition ...
Ana Barboza, Solène Ulmer-Moll, João Faria   +2 more
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Machine learning multi-classifiers for peptide classification

Neural Computing and Applications, 2008
In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology.
LUMINI, ALESSANDRA, NANNI, LORIS
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