Classification, Parameter Estimation and State Estimation

Abstract

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SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

“…In first phase, a learning algorithm runs over datasets to develop a model that could be employed in estimating an output. The aim of the model is to describe the relationship between the class and the predictor [15,20,13,30]. The quality of the produced model is assessed in the model testing phase.…”

Detection of pain from nociceptive laser-evoked potentials using single-trial analysis and pattern recognition

“…In this study, the feature extraction was performed based on the Bhattacharyya distance with Gaussian distributions [19], and the following feature classification was achieved based on the extracted feature vector ( z ) rather than the original feature vector ( β ). The advantages of this feature extraction are twofold: (1) the computation complexity of the feature classification will be greatly decreased by reducing the dimension of the feature vector, and (2) the classification accuracy will be increased by preventing possible overfitting of the contaminated noise.…”

“…The aim was achieved through the following steps: (1) estimating singletrial features of N2 and P2 in LEPs using advance singletrial analysis technique [18]; (2) extracting important features that would be optimally used to separate LEP trials from resting EEG trials; (3) classifying the extract important features using both linear and quadratic classifiers [19]; and (4) evaluating the performance of both classifiers using both error rate and receiver operator characteristic (ROC) curve.…”

3D Bicipital Groove Shape Analysis and Relationship to Tendopathy

“…9,10 We validate the discriminatory power of our shape descriptor by testing the performance of different classifiers attempting to classify normal versus pathological BG shapes (defined according to established 2D measurements taken in previous literature). 3,4,6,11 The other main contribution of this paper is a study of 32 images of patients presenting with various diagnoses of LBT pathology. In this study, we group the data sets according to expert diagnosis of LBT condition for each data set, and use the 3D shape of their BGs to determine shape differences between the groups.…”