Single- and Multi-order Neurons for recursive unsupervised learning

Single- and Multi-order Neurons for recursive unsupervised learning

Kiruthika Ramanathan (National University of Singapore, Singapore) and Sheng Uei Guan (Brunel University, UK)
Copyright: © 2008 |Pages: 17
DOI: 10.4018/978-1-59904-705-8.ch008
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In this chapter we present a recursive approach to unsupervised learning. The algorithm proposed, while similar to ensemble clustering, does not need to execute several clustering algorithms and find consensus between them. On the contrary, grouping is done between two subsets of data at one time, thereby saving training time. Also, only two kinds of clustering algorithms are used in creating the recursive clustering ensemble, as opposed to the multitude of clusterers required by ensemble clusterers. In this chapter a recursive clusterer is proposed for both single and multi order neural networks. Empirical results show as much as 50% improvement in clustering accuracy when compared to benchmark clustering algorithms.

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Table of Contents
Dimitris Vrakas, Ioannis Vlahavas
Chapter 1
Johan Baltié, Eric Bensana, Patrick Fabiani, Jean-Loup Farges, Stéphane Millet, Philippe Morignot, Bruno Patin, Gerald Petitjean, Gauthier Pitois, Jean-Clair Poncet
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Multi-Vehicle Missions: Architecture and Algorithms for Distributed Online Planning
Chapter 2
Antonio Garrido, Eva Onaindia
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Extending Classical Planning for Time: Research Trends in Optimal and Suboptimal Temporal Planning
Chapter 3
Roman Barták
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Principles of Constraint Processing
Chapter 4
Alexander Mehler
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Stratified Constraint Satisfaction Networks in Synergetic Multi-Agent Simulations of Language Evolution
Chapter 5
Zhao Lu, Jing Sun
As an innovative sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression... Sample PDF
Soft-constrained Linear Programming Support Vector Regression for Nonlinear Black-box Systems Identification
Chapter 6
Ioannis Partalas, Dimitris Vrakas, Ioannis Vlahavas
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Reinforcement Learning and Automated Planning: A Survey
Chapter 7
Stasinos Konstantopoulos, Rui Camacho, Nuno A. Fonseca, Vítor Santos Costa
This chapter introduces Inductive Logic Programming (ILP) from the perspective of search algorithms in Computer Science. It first briefly considers... Sample PDF
Induction as a Search Procedure
Chapter 8
Kiruthika Ramanathan, Sheng Uei Guan
In this chapter we present a recursive approach to unsupervised learning. The algorithm proposed, while similar to ensemble clustering, does not... Sample PDF
Single- and Multi-order Neurons for recursive unsupervised learning
Chapter 9
Malcolm J. Beynon
This chapter investigates the effectiveness of a number of objective functions used in conjunction with a novel technique to optimise the... Sample PDF
Optimising Object Classification: Uncertain Reasoning-Based Analysis Using CaRBS Systematic Research Algorithms
Chapter 10
P. Vasant, N. Barsoum, C. Kahraman, G.M Dimirovski
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Application of Fuzzy Optimization in Forecasting and Planning of Construction Industry
Chapter 11
Malcolm J. Beynon
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Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution
Chapter 12
Iker Gondra
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Parallelizing Genetic Algorithms: A Case Study
Chapter 13
Daniel Rivero, Miguel Varela, Javier Pereira
A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This... Sample PDF
Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
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