LNCS 2810 Classification of Protein Localisation Patterns via Supervised Neural Network Learning 1st Edition by Aristoklis Anastasiadis, George Magoulas, Xiaohui Liu  ISBN 3540452311 9783540452317

LNCS 2810 – Classification of Protein Localisation Patterns via Supervised Neural Network Learning 1st Edition by Aristoklis D. Anastasiadis, George D. Magoulas, Xiaohui Liu  – Ebook PDF Instant Download/Delivery. 3540452311, 9783540452317

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Product details:

ISBN 10: 3540452311

ISBN 13: 9783540452317

Author: Aristoklis D. Anastasiadis, George D. Magoulas, Xiaohui Liu

LNCS 2810 – Classification of Protein Localisation Patterns via Supervised Neural Network Learning 1st Edition: There are so many existing classification methods from diverse fields including statistics, machine learning and pattern recognition. New methods have been invented constantly that claim superior performance over classical methods. It has become increasingly difficult for practitioners to choose the right kind of the methods for their applications. So this paper is not about the suggestion of another classification algorithm, but rather about conveying the message that some existing algorithms, if properly used, can lead to better solutions to some of the challenging real-world problems. This paper will look at some important problems in bioinformatics for which the best solutions were known and shows that improvement over those solutions can be achieved with a form of feed-forward neural networks by applying more advanced schemes for network supervised learning. The results are evaluated against those from other commonly used classifiers, such as the K nearest neighbours using cross validation, and their statistical significance is assessed using the nonparametric Wilcoxon test.

 

LNCS 2810 – Classification of Protein Localisation Patterns via Supervised Neural Network Learning 1st Edition Table of contents:

1 Introduction

2 Classification Methods

2.1 The K Nearest Neighbours Algorithm

2.2 Multilayer Feed-forward Neural Networks and Supervised Learning

2.3 Ensemble-Based Methods

3 Experimental Study

3.1 Description of Datasets

3.2 Evaluation Methods

4 Results

4.1 Classifying E.coli Patterns Using a Feed-forward Neural Network

4.2 Classifying Yeast Patterns Using a Feed-forward Neural Network

4.3 Classifying Protein Patterns Using Ensemble-based Techniques

5 Conclusions

 

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