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What is Support Vector Machines

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
SVM map input vector to a higher dimensional space where a maximal hyperplane is constructed.
Published in Chapter:
Machine Learning in Morphological Segmentation
O. Lezoray (Universite de Caen Basse-Normandie, France), G. Lebrun (Universite de Caen Basse-Normandie, France), C. Meurie (INRETS-LEOST, France), C. Charrier (Universite de Caen Basse-Normandie, France), A. Elmotataz (Universite de Caen Basse-Normandie, France), and M. Lecluse (Centre Hospitalier Public du Cotentin, France)
DOI: 10.4018/978-1-60566-314-2.ch021
Abstract
The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis. Mathematical morphology is a very well established theory to process images. Segmentation by morphological means is based on watershed that considers an image as a topographic surface. Watershed requires input and marker image. The user can provide the latter but far more relevant results can be obtained for watershed segmentation if marker extraction relies on prior knowledge. Parameters governing marker extraction varying from image to image, machine learning approaches are of interest for robust extraction of markers. We review different strategies for extracting markers by machine learning: single classifier, multiple classifier, single classifier optimized by model selection.
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More Results
Fatigue Monitoring for Drivers in Advanced Driver-Assistance System
A support vector machine (SVM) is a classifier that separates classifiers by outputting a hyperplane. In supervised learning where labeled training data is given, SVM generates a hyperplane such that data belonging to same class will be on one side of hyperplane.
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From Analysis to Estimation of User Behavior
Support vector machines is a methodology of statistical learning theory, which is based on generating functions from a set of labeled training data.
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Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot
A supervised learning method with enhanced capabilities for dealing with complex, non-linear data to solve classification and regression problems.
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Classification in GIS Using Support Vector Machines
A supervised learning technique based on statistical learning theory.
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Analysis of System Marginal Price in the Turkish Electricity Market
It is one of the machine learning techniques to solve the classification problems.
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Computational Models for the Analysis of Modern Biological Data
machine learning algorithms that map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed.
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Digital Watermarking Techniques for Images: Survey
SVM is a method for creating functions from a set of labeled training data.
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Computational Intelligence for Pathological Issues in Precision Agriculture
A supervised learning algorithm that can be described as a feed-forward neural network with one hidden layer.
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On the Effectiveness of Social Tagging for Resource Discovery
A vector-based machine learning technique which makes use of the maximum distance between vector classes as a decision boundary.
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Machine Learning
Large margin models based on statistical learning theory.
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