Results 91 to 100 of about 869,216 (289)
Greedy Incremental Support Vector Regression [PDF]
Annals of computer science and information systems, 2019 Dymitr Ruta, Ling Cen, Quang Hieu Vu
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Developmental and Epileptic Encephalopathy due to Biallelic Pathogenic Variants in PIGM
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective PIGM encodes a critical enzyme in the glycosylphosphatidylinositol (GPI)‐anchor biosynthesis pathway. While promoter‐region mutations in PIGM have been associated with a relatively mild phenotype characterized by portal vein thrombosis and absence seizures, recent evidence suggests that coding‐region mutations result in a more severe
Júlia Sala‐Coromina +11 more
wiley +1 more source
Sequential support vector classifiers and regression [PDF]
, 1999 Support Vector Machines (SVMs) map the input training data into a high dimensional feature space and finds a maximal margin hyperplane separating the data in that feature space. Extensions of this approach account for non-separable or noisy training data
Vijayakumar, S., Wu, S.
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Current Tracking Adaptive Control of Brushless DC Motors
International Journal of Adaptive Control and Signal Processing, EarlyView.In this paper, the current tracking for Brushless Direct Current motors is approached considering uncertainty in the parameters of the motor's model. An adaptive control scheme to compensate electrical parameters uncertainty is proposed without requiring any knowledge of the mechanical parameters.
Fernanda Ramos‐García +3 more
wiley +1 more source
Detecting Anomalies in Meteorological Data Using Support Vector Regression
Advances in Meteorology, 2018 Significant errors exist in automated meteorological data, and identifying them is very important. In this paper, we present a novel method for determining abnormal values in meteorological observations based on support vector regression (SVR).
Min-Ki Lee +4 more
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Support Vector Regression for Surveillance Purposes
, 2006 This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer, we develop a localizer directly using the regression form of the Support Vector Machines (SVM).
Özer, Sedat +2 more
openaire +3 more sources
Incremental Support Vector Learning for Ordinal Regression
IEEE Transactions on Neural Networks and Learning Systems, 2015 Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR.
Gu, Bin +4 more
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Finding kernel function for stock market prediction with support vector regression [PDF]
, 2006 Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time.
Chai, Chon Lung
core
A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller
International Journal of Adaptive Control and Signal Processing, EarlyView.Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg +5 more
wiley +1 more source
From Regression to Classification in Support Vector Machines [PDF]
, 1998 We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters.
Evgeniou, Theodoros +2 more
core +3 more sources