61 research outputs found
Atlas selection strategy in multi-atlas segmentation propagation with locally weighted voting using diversity-based MMR re-ranking
In multi-atlas based image segmentation, multiple atlases with label maps are propagated to the query image, and fused into the segmentation result. Voting rule is commonly used classifier fusion method to produce the consensus map. Local weighted voting (LWV) is another method which combines the propagated atlases weighted by local image similarity. When LWV is used, we found that the segmentation accuracy converges slower comparing to simple voting rule. We therefore propose to introduce diversity in addition to image similarity by using Maximal Marginal Relevance (MMR) criteria as a more efficient way to rank and select atlases. We test the MMR re-ranking on a hippocampal atlas set of 138 normal control (NC) subjects and another set of 99 Alzheimer's disease patients provided by ADNI. The result shows that MMR re-ranking performed better than similarity based atlas selection when same number of atlases were selected
Design of Interactive Feature Space Construction Protocol
Machine learning deals with designing systems that learn from data i.e. automatically improve
with experience. Systems gain experience by detecting patterns or regularities and using them for
making predictions. These predictions are based on the properties that the system learns from the
data. Thus when we say a machine learns, it means it has changed in a way that allows it to
perform more efficiently than before. Machine learning is emerging as an important technology
for solving a number of applications involving natural language processing applications, medical
diagnosis, game playing or financial applications. Wide variety of machine learning approaches
have been developed and used for a number of applications.
We first review the work done in the field of machine learning and analyze various concepts
about machine learning that are applicable to the work presented in this thesis. Next we examine
active machine learning for pipelining of an important natural language application i.e.
information extraction, in which the task of prediction is carried out in different stages and the
output of each stage serves as an input to the next stage.
A number of machine learning algorithms have been developed for different applications.
However no single machine learning algorithm can be used appropriately for all learning
problems. It is not possible to create a general learner for all problems because there are varied
types of real world datasets that cannot be handled by a single learner. For this purpose an
evaluation of the machine learning algorithms is needed. We present an experiment for the
evaluation of various state-of-the-art machine learning algorithms using an interactive machine
learning tool called WEKA (Waikato Environment for Knowledge Analysis). Evaluation is
carried out with the purpose of finding an optimal solution for a real world learning problemcredit
approval used in banks. It is a classification problem.
Finally, we present an approach of combining various learners with the aim of increasing their
efficiency. We present two experiments that evaluate the machine learning algorithms for
efficiency and compare their performance with the new combined approach, for the same
classification problem. Later we show the effects of feature selection on the efficiency of our
combined approach as well as on other machine learning techniques. The aim of this work is to
analyze the techniques that increase the efficiency of the learners
NOTIFICATION !!!
All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
NOTIFICATION !!!
All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
NOTIFICATION !!!
All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
NOTIFICATION !!!
All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
NOTIFICATION !!!
All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
NOTIFICATION!!!
The full content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition
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