Andere Kunden interessierten sich auch für
![Solution of restricted two-dimensional fabric cutting problem]( "Solution of restricted two-dimensional fabric cutting problem")
Solution of restricted two-dimensional fabric cutting problem17,99 €![Feature Selection for High-Dimensional Data]( "Feature Selection for High-Dimensional Data")
Feature Selection for High-Dimensional Data41,99 €![Two-Dimensional Change Detection Methods]( "Two-Dimensional Change Detection Methods")
Two-Dimensional Change Detection Methods41,99 €![High-Dimensional Indexing]( "High-Dimensional Indexing")
High-Dimensional Indexing42,99 €![Design of a Three Dimensional Virtual Reality Environment]( "Design of a Three Dimensional Virtual Reality Environment")
Design of a Three Dimensional Virtual Reality Environment42,99 €![Three-Dimensional Computer Vision]( "Three-Dimensional Computer Vision")
Three-Dimensional Computer Vision83,99 €![Computer Vision: Three-dimensional Reconstruction Techniques]( "Computer Vision: Three-dimensional Reconstruction Techniques")
Computer Vision: Three-dimensional Reconstruction Techniques65,99 €
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.
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.