relevance vector machine technique
Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation
... It should be noted that for model calibration and verification using MARS, the same training and testing data sets previously used for the RVM modeling are utilized and MARS is implemented using the MATLAB software. ...
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CLASSIFICATION OF ELECTROCARDIOGRAM SIGNALS WITH SUPPORT VECTOR MACHINE AND RELEVANCE VECTOR MACHINE
... Feature reduction was achieved by the traditional AR modeling, commonly used in ECG signal classification. In particular, it can be seen that for all feature subspace dimensionalities except the lowest (i.e., 10 ...
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Through Wall Detection with Relevance Vector Machine
... In this paper, a machine-learning approach called RVM for through-wall detection has been proposed. It is characterized by its ability to capture the underlying physic of the system simply by examination of the ...
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Feature Selection for Image Retrieval based on Genetic Algorithm
... coherence vector and Gabor wavelets feature extraction ...and Relevance Feedback. The proposed system, feature vector of the image is extracted by calculation of color correlogram, Gabor filter and ...
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ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling
... Dimension reduction is essential to the regression anal- ysis of high-dimensional data. The idea in dimension reduction is to find a projection p to a k-dimensional sub- space, k ≪ p, such that less information is lost. ...
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Multi step approach for Speeding up Region Based Image Retrieval using Indexing technique
... support vector machine based learning with new random algorithm called firefly algorithm as a relevance feedback technique into region based image retrieval system ...
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A Novel Intrusion Detection System Based On Kpca And Rvm With Pso Model
... detection technique and learning ...The Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for ...
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A Robust Nonlinear Beamforming Assisted Receiver for BPSK Signalling
... the relevance vector machine (RVM) principle for classification ...clustering technique and the re- cursive least squares (CRLS) algorithm [10],[11] are consid- ...
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Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture
... with machine learning algorithms (RVM) to estimate chlorophyll concentration as an important biophysical parameter to be used in precision agricul- ...modeling technique, coupled with cross validation and ...
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Relevance vector machine for optical diagnosis of cancer
... of relevance vector machine (RVM) [33], a recent Bayesian machine-learning frame- work of statistical pattern recognition, for development of a probability based diagnostic algorithm for ...
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Modeling of compressive strength of masonry structure using relevance vector machine and minimax probability machine regression
... mines a bound on the probability that the regression model is within of the true regression function. MPMR has been developed based on mean and covariance matrix statistics of the regression data. MPMR employs the ...
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Fault classification in dynamic processes using multiclass relevance vector machine and slow feature analysis
... FDA for fault classification [12]. However, Chiang et al., who investigated the advantages of FDA and support vector machine (SVM), showed that nonlinear SVM outperforms FDA [13]. Jing and Hou studied the ...
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An Integrated Framework for Designing Content based Image Retrieval System using Support Vector Machine with Enhanced Biased Maximum Margin
... support vector machine. As the previous approaches used with the relevance feedback suffers a major ...sampling technique used ...different technique of sampling other than ...effective ...
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Relevance vector machine based fault classification in wind energy conversion system
... This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a ...
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The Support Vector Machine Technique for Islanding Detection in Distributed Generation
... support vector machine constructs a hyper plane or set of hyperplanes in a high- or infinite- dimensional space, which can be used for classification, regression, or other ...
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Distribution-Sensitive Learning on Relevance Vector Machine for Pose-Based Human Gesture Recognition
... Many real-world gesture datasets are by nature containing unbalanced number of poses across classes. Such imbalance severely reduces bag-of-poses based classification performance. On the other hand, collecting a dataset ...
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Detection of Epileptic Seizure Based on Phase Space Reconstruction and Support Vector Machine
... Where (k= 1, 2, ..., n) are the output coefficients, and n is the number of previous samples, , and denotes the zero mean process. The AR coefficients were averaged, and then collected with FFT features in a single ...
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An optimal experimental design perspective on redial basis function regression
... obtained from both the D-optimality criterion (left) and the Relevance vector machine approach (right). Again, while it is obvious that the two methods are looking for the points that most affect the ...
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An Effective Machine Learning Approach For Disease Predictive Modelling In Medical Application
... addition, we advanced the predictive model by validating the disease results using supervised machine learning methods and achieved good accuracy. For the advancement of the predictive model, we used three ...
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Applications of different Techniques in Agricultural System: A Review
... Isaac Kofi Nti, Yeboah Samuel Jonas and Gyamfi Eric in paper “Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach” proposed computer vision techniques to uncover the affected spots ...
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