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46
COMPARISON OF DIFFERENT MACHINE LEARNING CLASSIFIERS FOR BUILDING EXTRACTION IN LIDAR-DERIVED DATASETS
"... ABSTRACT: Building extraction in remotely sensed imagery is an important problem that needs solving. It can be used to aid in urban planning, hazard assessments and disaster risk management among others. Light Detection and Ranging or LiDAR, is one of the most powerful remote sensing technologies no ..."
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surface openness. These objects are then classified using different machine learning classifiers such as Support Vector Machines, K-Nearest Neighbors, Naïve Bayes Classifier, Decision Trees, and Random Forests. A comparative assessment was done on the performance of these different machine learning
Exploring a novel method for face image gender Classification using Random Forest and comparing with other Machine Learning Techniques
"... Gender classification such as classifying human face is not only challenging for computer, but even hard for human in some cases. This Paper use ORL database contain 400 images include both Male and Female Gender. Our experimental results show the superior performance of our approach to the existing ..."
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to the existing gender classifiers. We achieves excellent classification (100%) accuracy using approach (Continuous wavelet Transform and Random Forest) and compared with other classification Technique like Support Vector Machine, linear discriminate analysis, k- nearest neighbor, Fuzzy c – means, Fuzzy c
Experimental Comparisons of Multi-class Classifiers
, 2014
"... The multi-class classification algorithms are widely used by many areas such as machine learning and computer vision domains. Nowadays, many literatures described multi-class algorithms, however there are few literature that introduced them with thorough theoretical analysis and experimental compari ..."
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support vector machines, K nearest neighbors, multi-class logistic classifier, multi-layer perceptron, naive Bayesian classifier and conditional random fields classifier. The experiment tested on five data sets: SPECTF heart data set, Ionosphere radar data set, spam junk mail filter data set, optdigits
Urban Land Cover Classification Using Support Vector Machine
"... Abstract. This research investigates the various RADARSAT-2 polarimetric SAR features for ur-ban land cover classification using object-based method combining with support vector machine (SVM) and ruled-based approach. Six-dates of RADARSAT-2 fine-beam polarimetric SAR data were acquired in the rura ..."
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Abstract. This research investigates the various RADARSAT-2 polarimetric SAR features for ur-ban land cover classification using object-based method combining with support vector machine (SVM) and ruled-based approach. Six-dates of RADARSAT-2 fine-beam polarimetric SAR data were acquired
Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
"... We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least square ..."
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We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least
A Comparison of Machine Learning Algorithms for Classification of Tropical Ecosystems Observed by Multiple Sensors at Multiple Scales
"... Abstract – A substantial number of studies compare conventional classifiers (e.g. Maximum Likelihood, Decision Trees, Neural Networks or Support Vector Machines (SVM)) in a single location. We propose here an in-depth comparison of classifications by assessing the potential of SVM (often the “winner ..."
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the “winner ” in the previously mentioned studies) versus a range of the machine learning algorithms developed during the last decade: Naïve Bayes, C4.5 algorithm, Random Forest, Regression Tree and k-Nearest Neighbor. They were tested over different ecosystems across Moorea Island (French Polynesia) using
Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques
"... Abstract — The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifi ..."
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Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques
Machine Learning for Imbalanced Datasets: Application in Medical Diagnostic
"... In this paper, we present a new rule induction algorithm for machine learning in medical diagnosis. Medical datasets, as many other real-world datasets, exhibit an imbalanced class distribution. However, this is not the only problem to solve for this kind of datasets, we must also consider other pro ..."
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conducted using an original dataset for cardiovascular diseases diagnostic and three public datasets. The experiments are performed using standard classifiers (Naïve Bayes, C4.5 and k-Nearest Neighbor), emergent classifiers (Neural Networks and Support Vector Machines) and other classifiers used
Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers
"... supervised classification techniques result in very different probabilistic transfer functions when applied to the same scribbles in the volumetric domain. We provide a comprehensive comparison and guidelines for use. Complex volume rendering tasks require high-dimensional transfer functions, which ..."
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previous intelligent system approach to volume rendering, and we system-atically compare five supervised classification techniques – Gaussian Naive Bayes, k Nearest Neighbor, Support Vector Machines, Neural Networks, and Random Forests – with respect to probabilistic classification, support for multiple
Machine Learning based Approach for Protein Function Prediction using Sequence Derived Properties
"... Protein function prediction is an important and challenging field in Bioinformatics. There are various machine learning based approaches have been proposed to predict the protein functions using sequence derived properties. In this paper 857 sequence-derived features such as amino acid composition, ..."
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, dipeptide composition, correlation, composition, transition and distribution and pseudo amino acid composition are used with various machine learning based approaches such as Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and fuzzy k-Nearest Neighbor (k-NN) to predict the protein
Results 1 - 10
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46
