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Dynamic ranking learning problem is considered when the training sample is a data stream, consisting of a sequence of a series of objects characterized by a set of features and relative ranks within each series. The problem is reduced to preference learning to rank on clusters in the feature space of ranked objects, while aggregated training dataset is formed from the centers of clusters and estimates...
Adverse hospital patient outcomes due to deterioration are often preceded by periods of physiological deterioration that is evident in the vital signs, such as heart rate, respiratory rate, etc. Clinical practice currently relies on periodic, manual observation of vital signs, which typically occurs every 2-to-4 hours in most hospital wards, and so patient deterioration may go unidentified. While...
In this work we have reformulated the twin support vector machine (TWSVM) classifier by considering unity norm of the normal vector of the hyperplanes as the constraints. TWSVM with unity norm hyperplanes removes the shortcomings of the classical TWSVM formulation. The resulting new formulation is a nonlinear programming problem which is solved by sequential quadratic optimization method. The performance...
This paper presents a machine learning approach, namely the Support Vector Machine (SVM), to solve a particular localization problem. The problem is to ascertain whether an object carrying a localization tag is inside or outside a particular area. As the area becomes smaller and as the object approaches the boundaries of the area, even minute errors can result in a completely wrong estimation. SVM...
In the process of cost prediction modeling with support vector machine (SVM), the prediction accuracy is significantly impacted by the similarity between training samples and the predicted object. In traditional cost prediction modeling, the training data must be independent and identically distributed and every sample participating in training is treated equally. However, different samples owe the...
Classification problems in critical applications such as health care or security often require very high reliability because of the high costs of errors. In order to achieve this reliability, such systems often require the use of sequential inspections, where additional data can be collected to resolve ambiguous test cases. It is impractical or costly to collect this additional data on every sample,...
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed general approach to formalizing such problems, known as learning...
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper is classifying the scenes using support vector machine with radial basis kernel with...
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