Books > Computing & IT > General theory of computing
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High Dimensional Clustering and Applications of Learning Methods (Paperback)
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High Dimensional Clustering and Applications of Learning Methods (Paperback)
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This book is divided into two parts. The first part is about
non-redundant clustering and feature selection for high dimensional
data. The second part is on applying learning techniques to lung
tumor image-guided radiotherapy. In the first part, a new
clustering paradigm is investigated for exploratory data analysis:
find all non-redundant clustering views of the data. Also a feature
selection method is developed based on the popular transformation
approach: principal component analysis (PCA). In the second part,
machine learning algorithms are designed to aid lung tumor
image-guided radiotherapy (IGRT). Specifically, intensive studies
are preformed for gating and for directly tracking the tumor. For
gating, two methods are developed: (1) an ensemble of templates
where the representative templates are selected by Gaussian mixture
clustering, and (2) a support vector machine (SVM) classifier with
radial basis kernels. For the tracking problem, a multiple-
template matching method is explored to capture the varying tumor
appearance throughout the different phases of the breathing cycle.
General
Imprint: |
Lap Lambert Academic Publishing
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Country of origin: |
Germany |
Release date: |
April 2009 |
First published: |
June 2010 |
Authors: |
Ying Cui
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Dimensions: |
229 x 152 x 9mm (L x W x T) |
Format: |
Paperback - Trade
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Pages: |
160 |
ISBN-13: |
978-3-8383-0080-1 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
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LSN: |
3-8383-0080-7 |
Barcode: |
9783838300801 |
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