kernel extreme learning machine (KELM)
Multi label Learning Based on Kernel Extreme Learning Machine
... multi-label learning algorithm KELM-ML is designed based on the kernel extreme learning machine, and an adaptive threshold function is set ...
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Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM+
... empirical kernel mapping-based kernel extreme learning machine plus (EKM–KELM + ) classifier to discriminate different blood pressure grades in adults from structural brain MR ...
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Hybridization Of Stdl With Optimal Kernel Extreme Learning Machine (Okelm) Based Short Term Crude Oil Price Forecasting In Commodity Futures Market
... Abstract: Futures markets offer contemporary price quotations for a group of contracts with maturities twelve or more months in the future. It coherently determines price levels and price variations suitable for contract ...
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A Real-Time Automatic Method for Target Locating Under Unknown Wall Characteristics in through -Wall Imaging
... on kernel extreme learning machine (KELM) is proposed in this ...vector machine (SVM) and least-squares support vector machine (LS-SVM), the KELM provides almost the same ...
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Kernel-Based Multilayer Extreme Learning Machines for Representation Learning
... ABSTRACT: Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly ...
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Title: Recognition of Human Facial Expression using Machine Learning Algorithm
... The kernel extension of the Extreme Sparse Learning framework is Kernel Extreme Sparse Learning ...of extreme sparse learning machine (ELM) by turning ...
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Recent Trends in ELM and MLELM: A review
... effective learning techniques (algorithms). The Extreme Learning Machine as an evolving learning technique gives efficient unified solutions to generalize feed-forward networks ...
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Recognition of Facial Structures Using Extreme Learning Machine Algorithm
... simple learning algorithm for SLFNs called extreme learning ...traditional learning algorithms the extreme learning algorithm not only provides the smaller training error but ...
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An Extreme Learning Machine for Biomedical Image classification: A Review
... and machine learning method; the predicted diagnostic accuracy was 97% and the actual diagnostic accuracy was 100% in 118 new ...the kernel regions in the images and then using these regions as ...
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Kernelized Extreme Learning Machine with Levenberg Marquardt Learning Approach towards Intrusion Detection
... times, Extreme Learning Machine (ELM) has been extensively applied to provide potential solutions for the IDS ...basis kernel function is tuned using Levenberg-Marquardt (LM) learning ...
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Regularized minimum class variance extreme learning machine for language recognition
... traditional kernel methods can also be applied to ELM ...variance extreme learning machine (MCVELM) was proposed for human action recognition and achieved excellent ...
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Kernel Based Extreme Learning Machine in Identifying Dermatological Disorders
... using kernel function. It uses kernel trick in which, in the feature space no need to calculate highly non linear kernel ...to kernel trick only scalar product is required in feature ...
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A Survey on Stock Market Prediction Using SVM
... will go up or down in the upcoming year? In the modern techniques, one way is to develop a predictor based on the information in the historical data. First of all, we should select some major factors that may influence ...
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FEATURE SELECTION USING MODIFIED ANT COLONY OPTIMIZATION APPROACH (FS MACO) BASED FIVE LAYERED ARTIFICIAL NEURAL NETWORK FOR CROSS DOMAIN OPINION MINING
... and machine learning can result in higher True Positive Rate, lower False positives, and better ...and machine learning methods that have been proposed by researchers in the few past years, ...
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A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter
... Fig. 7 illustrates a part of sampled waveform. In this figure, the horizontal axis represents the number of sam- pling point, which is also the time window width in this study. As seen, when the sampling number is 100, ...
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A sparse multinomial probit model for classification
... of machine learning methods for classifying objects into pre-determined groups consider only two-class or binary problems but many tasks involve more than two classes – so called multinomial ...
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A Novel Approach for Facial Emotion Recognition Based On Kernel Extreme Sparse Learning with CSMP
... The probe image in the module represents an image from the testing data set and blur is applied on image by using “imfilter” function and type blur is Gaussian blurring.. TSF [r] ...
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Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine
... modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT ...classical machine learning technologies are employed ...
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Application of Extreme Learning Machine in Fault Classification of Power Transformer
... Single layer feed forward Neural Network is modeled with hidden neurons and activation function by Guang- Bin et al. [9]. Extreme learning machine for RBF (Radial Basis Function) network produces ...
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Extreme Learning Machine for Robustness
... In the spectra shift simulation test, ELM and peak tracking method are used to analysis the 300. spectra shift[r] ...
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