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42 results on '"Birkenhead, Ralph"'

2. Dimensionality Reduction and Microarray Data

Author

Elizondo, David A., Passow, Benjamin N., Birkenhead, Ralph, Huemer, Andreas, Barth, Timothy J., editor, Griebel, Michael, editor, Keyes, David E., editor, Nieminen, Risto M., editor, Roose, Dirk, editor, Schlick, Tamar, editor, Gorban, Alexander N., editor, Kégl, Balázs, editor, Wunsch, Donald C., editor, and Zinovyev, Andrei Y., editor

Published
2008
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3. On the Generalization of the m-Class RDP Neural Network

Author

Elizondo, David A., Ortiz-de-Lazcano-Lobato, Juan M., Birkenhead, Ralph, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kůrková, Véra, editor, Neruda, Roman, editor, and Koutník, Jan, editor

Published
2008
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4. A Novel and Efficient Method for Testing Non Linear Separability

Author

Elizondo, David, Ortiz-de-Lazcano-Lobato, Juan Miguel, Birkenhead, Ralph, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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13. Dimensionality Reduction and Microarray Data.

Author

Barth, Timothy J., Griebel, Michael, Keyes, David E., Nieminen, Risto M., Roose, Dirk, Schlick, Tamar, Gorban, Alexander N., Kégl, Balázs, Wunsch, Donald C., Zinovyev, Andrei Y., Elizondo, David A., Passow, Benjamin N., Birkenhead, Ralph, and Huemer, Andreas

Abstract

Microarrays are being currently used for the expression levels of thousands of genes simultaneously. They present new analytical challenges because they have a very high input dimension and a very low sample size. It is highly complex to analyse multi-dimensional data with complex geometry and to identify low-dimensional "principal objects" that relate to the optimal projection while losing the least amount of information. Several methods have been proposed for dimensionality reduction of microarray data. Some of these methods include principal component analysis and principal manifolds. This article presents a comparison study of the performance of the linear principal component analysis and the non linear local tangent space alignment principal manifold methods on such a problem. Two microarray data sets will be used in this study. A classification model will be created using fully dimensional and dimensionality reduced data sets. To measure the amount of information lost with the two dimensionality reduction methods, the level of performance of each of the methods will be measured in terms of level of generalisation obtained by the classification models on previously unseen data sets. These results will be compared with the ones obtained using the fully dimensional data sets. [ABSTRACT FROM AUTHOR]

Published
2007
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41. Application of Soft-Computing Techniques in Modelling of Buildings

Author

Djamel Azzi, Barry Haynes, Alexander Gegov, Khalil Ibrahim Hady Alkadhimi, G.S. Virk, John, Robert, and Birkenhead, Ralph

Subjects
Soft computing, Building management system, soft-computing, Engineering, Mathematical optimization, Artificial neural network, business.industry, building management systems, Computing, intelligent modeling, Fuzzy logic, Problem domain, Genetic algorithm, Combinatorial search, Artificial intelligence, business, Predictive modelling, Computer Science(all)
Abstract

The paper presents recent results on the application of soft computing techniques for predictive modelling in the built sector. More specifically, an air-conditioned zone (Anglesea Building, University of Portsmouth), a naturally ventilated room (Portland Building, University of Portsmouth), and an endothermic building (St Catherine’s Lighthouse, Isle of Wight) are considered. The zones are subjected to occupancy effects and external disturbances which are difficult to predict in a quantitative way and hence the soft computing approach seems to be a better alternative. In fact, the overall complexity of the problem domain makes the modelling of the internal climate in buildings a difficult task which is not always carried out in a satisfactory way by traditional deterministic and stochastic methods. The approach adopted uses fuzzy logic for modelling, as well as neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The latter is of the Takagi-Sugeno type and it is built by subtractive clustering as a result of which the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of a combinatorial search over all possible fuzzy model structures for a specified plant order is presented. The model parameters are further adjusted by a back-propagation neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Modelling results with actual data from the three buildings are presented where the initial (fuzzy) and the final (fuzzy-neuro and fuzzy-genetic) models are shown.

Published
2001

42. Finding Diverse Examples Using Genetic Algorithms

Author

Colin G. Johnson, John, Robert, and Birkenhead, Ralph

Subjects
Set (abstract data type), Class (set theory), Theoretical computer science, business.industry, Random string, Total function, Computer programming, Object (computer science), business, QA76, Mathematics
Abstract

A number of real-world problems can be seen as instances of the general problem of emphfinding qualitative examples. In such a problem we know how to classify objects from a set into a large number of classes, and we would like to find one specific example for each class. In this paper we outline a number of problems which fit into this category, and analyse some of the requirements for a general heuristic for this category of problem. We then develop a heuristic for this type of problem, based on genetic algorithms, and investigate the application of this heuristic to some test problems.

Published
2001

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