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Bauer, E. and Kohavi, R. (1999), "An empirical comparison of voting classification algorithms: Bagging, boosting, and variants", Machine Learning, vol. 36, no. 1, pp. 105-139. Belousov, A., Verzakov, S. and Von Frese, J. (2002), "A flexible classification approach with optimal generalisation performance: support vector machines", Chemometrics and Intelligent Laboratory Systems, vol. 64, no. 1, pp. 15-25. Belousov, A. I., Verzakov, S. A. and von Frese, J. (2002), "Applicational aspects of support vector machines", Journal of Chemometrics, vol. 16, no. 8-10, pp. 482- 489. Ben-Hur, A. and Weston, J. (2010), "A User's Guide to Support Vector Machines", in Carugo, O. and Eisenhaber, F. (eds.) Humana Press, , pp. 223-239. Bennett, K. P. and Campbell, C. (2003), "Support Vector Machines: Hype or Hallelujah?", SIGKDD Explorations, vol. 2, pp. 2000. Berger, S. I., Iyengar, R. and Ma'ayan, A. (2007), "AVIS: AJAX viewer of interactive signaling networks", Bioinformatics, vol. 23, no. 20, pp. 2803-2805. Berners-Lee, T., Hendler, J. and Lassila, O. (2001), "The semantic web", Scientific American, vol. 284, no. 5, pp. 28-37. Berners-Lee, T., ( 1996), WWW: Past, Present, and Future. Berners-Lee, T. and Fischetti, M. (2008), Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor, Paw Prints. Bessant, C., Brereton, R.,G. and Dunkerley, S. (1999), "Integrated processing of triply coupled diode array liquid chromatography electrospray mass spectrometric signals by chemometric methods", Analyst, vol. 124, no. 12, pp. 1733-1744. Boardman, M. and Trappenberg, T. (2006), "A Heuristic for Free Parameter Optimization with Support Vector Machines", Neural Networks, 2006. IJCNN '06. International Joint Conference on, pp. 610. Boser, B. and Vapnik, V. N. (1992), "A training algorithm for optimal margin classifiers", Proceedings of the fifth annual workshop on Computational learning theory, , pp. 144-152. Boser, B. E., Guyon, I. M. and Vapnik, V. N. (1992), "A training algorithm for optimal margin classifiers", Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, United States, ACM, New York, NY, USA, pp. 144. Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Jackel, L. D., LeCun, Y., Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V. (1994), "Comparison of classifier methods: a case study in handwritten digit recognition", Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image 203
Processing., Proceedings of the 12th IAPR International. Conference on, Vol. 2, pp. 77. Boulesteix, A. -. (2004), "PLS dimension reduction for classification with microarray data", Statistical applications in genetics and molecular biology, vol. 3, no. 1. Boulesteix, A. -., Lambert-Lacroix, S., Peyre, J. and Strimmer, K. (2011), plsgenomics: PLS analyses for genomics, available at: http://CRAN.Rproject.org/package=plsgenomics. Box, M. J. (1965), "A New Method of Constrained Optimization and a Comparison With Other Methods", The Computer Journal, vol. 8, no. 1, pp. 42-52. Bragin, E. (2012), Interactive genome browser based on html5 and related web technologies (Master of Science thesis), Cranfield Univerity, Cranfield Health. Brereton, R. G. (2006), "Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data", TrAC Trends in Analytical Chemistry, vol. 25, no. 11, pp. 1103. Brereton, R. G. (2009), Chemometrics for pattern recognition, Wiley. Brereton, R.G. and Lloyd, G.R., ( 2009), Support Vector Machines for classification and regression, The Royal Society of Chemistry. Burges, C. J. C. (1998), "A tutorial on support vector machines for pattern recognition", Data mining and knowledge discovery, vol. 2, no. 2, pp. 121-167. Butcher, E. C., Berg, E. L. and Kunkel, E. J. (2004), "Systems biology in drug discovery", Nature biotechnology, vol. 22, no. 10, pp. 1253-1259. Byvatov, E. and Schneider, G. (2003), "Support vector machine applications in bioinformatics", Appl Bioinformatics, vol. 2, no. 2, pp. 67-77. Calì, A., Calvanese, D., De Giacomo, G. and Lenzerini, M. (2004), "Data integration under integrity constraints", Information Systems, vol. 29, no. 2, pp. 147-163. Canty, A. "An S-Plus Library for Resampling Methods", . Canty, A. and Ripley, B. D. (2012), boot: Bootstrap R (S-Plus) Functions, . Carney, S. L. (2003), "Leroy Hood expounds the principles, practice and future of systems biology", Drug discovery today, vol. 8, no. 10, pp. 436-438. Cawley, G. C. and Talbot, N. L. C. (2007), "Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters", J.Mach.Learn.Res., vol. 8, pp. 841-861. CellML , The CellML project - CellML, available at: http://www.cellml.org. 204
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CRANFIELD UNIVERSITY Eleni Anthippi
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ABSTRACT Muscle foods such as meat,
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TABLE OF CONTENTS ABSTRACT ........
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6.2.4 The iWebPlots package .......
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Figure 3-12 Speedup produced by the
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Figure 6-9 Sweave example for the d
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TABLE OF EQUATIONS Equation 1 Mean-
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RMSE RMSECV SSE SVD SVMs SYMBIOSIS-
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Figure 1-1 Evolution from molecular
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1.1.2.1 Fourier Transform Infrared
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1.1.3 Microbial Spoilage in Meat Sy
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The multivariate techniques applied
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1.4 Multivariate Analysis: Unsuperv
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1.4.2 Cluster Analysis Cluster anal
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1.5 Multivariate Analysis: Supervis
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In any linearly separable binary da
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Where ( ) are the Lagrange multipli
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Every kernel is characterised by a
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The “one-against-all” approach
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Furthermore, metrics such as the bi
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1.6.3 Leave-One-Out Cross-Validatio
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In such cases, the model becomes qu
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1.8 Aims and objectives The overall
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2 Development of the multivariate a
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Sensory panel scores were of the hi
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DF (Hz) 2.2.1.5 Electronic Nose (e-
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Figure 2-3 Data intersection The fi
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3. Validation Techniques In the cas
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Figure 2-4 The process of construct
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2.3 Results and Discussion 2.3.1 Pr
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(b) HPLC data (c) e-nose data Figur
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In the case of FTIR, linear classif
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2.3.2.2 Class Prediction Accuracies
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Figure 2-7 Class prediction rates o
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The topology of the three-dimension
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Figure 2-9 Three-dimensional error
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Furthermore, a thorough comparison
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In the master/slave architecture, a
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precision of the classification acc
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Figure 3-3 The steps of the Nelder-
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3.3 Results and Discussion 3.3.1 Li
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3.3.2 Nonlinear Models In order to
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9) 10) 11) 12) 13) 14) 15) 16) Figu
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As an additional visual aid, these
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a) Number of iterations b) Number o
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Figure 3-10 Comparison of the execu
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Figure 3-12 Speedup produced by the
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4 Integration of Heterogeneous Data
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4.2.2 Procrustes Analysis Procruste
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symmetric method since the ordering
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Figure 4-3 Steps of CPCA for the da
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4.2.5 Data Integration and Analysis
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Figure 4-4 Data integration workflo
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a) GPA b) CPCA Figure 4-6 The conse
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accuracies as the linear models; po
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Figure 4-8 Classification Results f
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Similar to FTIR, the PLS-DA ensembl
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4.3.3 Permutation Tests Even though
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Even though the interpretation of t
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Figure 4-12 Distribution plots of t
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RBF SVMs Datasets Original %CC Mean
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Figure 4-14 Boxplots representing t
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5 Application of the multivariate a
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Figure 5-1 Mean FTIR spectra for ca
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5.2.2.2 Fourier Transform Infrared
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5.2.2.4 Data Overview For each expe
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5.2.3.3 High Throughput Liquid Chro
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Figure 5-5 displays the PCA scores
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(a) GPA (b) CPCA Figure 5-6 The con
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observation confirms that indeed CP
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(a) RBF SVMs (b) PLS-DA Figure 5-9
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Figure 5-12 Execution times of the
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(a) FTIR data (b) Raman data Figure
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The classification results for the
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Figure 5-16 Class prediction rates
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(a) RBF SVMs (b) PLS-DA Figure 5-17
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Figure 5-20 Execution times of the
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(a) FTIR data (b) HPLC data 148
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(a) GPA (b) CPCA Figure 5-22 The co
Page 167 and 168: The classification results of the f
Page 169 and 170: Figure 5-24 Classification Results
Page 171 and 172: Figure 5-25 Class prediction rates
Page 173 and 174: The permutation results for the dat
Page 175 and 176: Figure 5-28 Distribution plots of t
Page 177 and 178: RBF SVMs Datasets Original %CC Mean
Page 179 and 180: Figure 5-30 Boxplots representing t
Page 181 and 182: 5.4 Comparison of the individual ca
Page 183 and 184: Figure 5-32 Investigating the commo
Page 185 and 186: 6 Development of improved visualisa
Page 187 and 188: 6.2.2 Generating static graphs As d
Page 189 and 190: Figure 6-2 Construction process of
Page 191 and 192: 6.2.2.2 Generating dynamic reports
Page 193 and 194: activity, while simultaneously the
Page 195 and 196: or disappeared (Wang et al., 2008).
Page 197 and 198: ectangles, circles and/or irregular
Page 199 and 200: In addition to the static figures g
Page 201 and 202: c) graphics package d) ggplot2 pack
Page 203 and 204: In addition to aesthetics, faceting
Page 205 and 206: Datasets FTIR HPLC e-nose PLS-DA (L
Page 207 and 208: Figure 6-10 Interactive dendrogram
Page 209 and 210: 6.4 Conclusion The work reported in
Page 211 and 212: large number of individual models i
Page 213 and 214: The generated suite of tools, as pr
Page 215 and 216: In addition, the difficulty to corr
Page 217: REFERENCES Almeida, J. A. S., Barbo
Page 221 and 222: Clarke, B., Fokoué, E. and Zhang,
Page 223 and 224: spectroscopy and machine learning",
Page 225 and 226: Gower, J. C. (1975), "Generalized p
Page 227 and 228: Jardon, M. (2006), Systems Biology:
Page 229 and 230: Nicolaou, N., Xu, Y. and Goodacre,
Page 231 and 232: Shah, A. A., Barthel, D., Lukasiak,
Page 233 and 234: Vera, G., Jansen, R. and Suppi, R.
Page 235: Xu, Y. and Goodacre, R. (2012), "Mu
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