Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water
نویسندگان
چکیده
منابع مشابه
Machine Learning with Lipschitz Classifiers DISSERTATION
The classification of complex patterns is one of the most impressive cognitive achievements of the human brain. Humans have the ability to recognize a complex image, like for example that of a known person, and to distinguish it from other objects within half a second. While for a solution of this task the brain has access to a massive parallelism and a vast, hierarchically organized, and auto-...
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Recently there has been a strong development in machine learning classification approaches for analyzing brain activity patterns. The main goal of these approaches is to reveal the information represented in voxels of the neurons and classify them in relevant classes. The functional magnetic resonance imaging (fMRI) has provided researchers with detailed three dimensional images of a human brai...
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This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with differen...
متن کاملComparing Common Machine Learning Classifiers in Low-dimensional Feature Vectors for Brain Computer Interface Applications
There are lots of classification and feature extraction algorithms in the field of brain computer interface. It is significant to use optimal classification algorithm and fewer features to implement a fast and accurate brain computer interface system. In this paper, we evaluate the performances of five classical classifiers in different aspects including classification accuracy, sensitivity, sp...
متن کاملComparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.
PURPOSE To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained...
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2019
ISSN: 2072-4292
DOI: 10.3390/rs11111351