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Learning to classify in large committee machines
Physical Review E, 1994The 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 Comparison of Machine Learning Classifiers
Advanced Materials Research, 2011A 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 +4 more
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Classifying protein kinase conformations with machine learning
Protein ScienceAbstractProtein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG‐in, DFG‐out, and some rarer conformations.
Ivan Reveguk, Thomas Simonson
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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 +2 more
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Machine learning classifiers using stochastic logic
2016 IEEE 34th International Conference on Computer Design (ICCD), 2016This 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 +3 more
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Classifying Glaucoma Using Machine Learning Techniques
2023ABSTRACTGlaucoma is a common eye disease that can lead to blindness if not detected and treated early. In this paper, we present a machine learning-based approach for classifying glaucoma. We use a publicly available dataset of retinal images and extract features using convolutional neural networks.
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Predicting Learning Styles Using Machine Learning Classifiers
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022Khadeja Ali Hamamy Assiry +1 more
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