Abstract
In many Pattern Recognition applications, the achievement of acceptable recognition rates is conditioned by the large pattern variability, whose distribution cannot be simply modeled.
This affects the results at each stage of the recognition system so that, once this has been designed, its performance cannot be improved over a certain bound, despite the efforts in refining either the classification or the description method.
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References
International workshop on multiple classifier systems. Web Page, http://www.diee.unica.it/mcs/
Ahmad, A., Brown, G.: A study of random linear oracle ensembles. In: Benediktsson, et al. [12], pp. 488–497
Alam, H., Rahman, A.F.R., Tarnikova, Y.: Solving problems two at a time: Classification of web pages using a generic pair-wise multiple classifier system. In: Windeatt, Roli [134], pp. 385–394
Alpaydin, E.: Introduction To Machine Learning. MIT Press (2004)
Ariu, D., Giacinto, G.: A modular architecture for the analysis of http payloads based on multiple classifiers. In: Sansone, et al. [112], pp. 330–339
Asdornwised, W., Jitapunkul, S.: Automatic target recognition using multiple description coding models for multiple classifier systems. In: Windeatt, Roli [134], pp. 336–345
Azizi, N., Farah, N., Sellami, M., Ennaji, A.: Using diversity in classifier set selection for arabic handwritten recognition. In: Gayar, et al. [52], pp. 235–244
Azmy, W.M., Atiya, A.F., El-Shishiny, H.: Forecast combination strategies for handling structural breaks for time series forecasting. In: Gayar, et al. [52], pp. 245–253.
Azmy, W.M., El Gayar, N., Atiya, A.F., El-Shishiny, H.: Mlp, gaussian processes and negative correlation learning for time series prediction. In: Benediktsson, et al. [12], pp. 428–437
Batista, L., Granger, E., Sabourin, R.: A multi-classifier system for off-line signature verification based on dissimilarity representation. In: Gayar, et al. [52], pp. 264–273
Batista, L., Granger, E., Sabourin, R.: Dynamic ensemble selection for off-line signature verification. In: Sansone, et al. [112], pp. 157–166
Benediktsson, J.A., Kittler, J., Roli, F. (eds.): MCS 2009. LNCS, vol. 5519. Springer, Heidelberg (2009)
Benediktsson, J.A., Sveinsson, J.R.: Consensus based classification of multisource remote sensing data. In: Kittler, Roli [73], pp. 280–289
Benfenati, E., Mazzatorta, P., Neagu, D., Gini, G.C.: Combining classifiers of pesticides toxicity through a neuro-fuzzy approach. In: Roli, Kittler [107], pp. 293–303
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The konstanz information miner. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) GfKl. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 319–326. Springer (2007)
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B.: KNIME - the konstanz information miner: version 2.0 and beyond. SIGKDD Explorations 11(1), 26–31 (2009)
Bertolami, R., Bunke, H.: Multiple classifier methods for offline handwritten text line recognition. In: Haindl, et al. [59], pp. 72–81
Biggio, B., Corona, I., Fumera, G., Giacinto, G., Roli, F.: Bagging classifiers for fighting poisoning attacks in adversarial classification tasks. In: Sansone, et al. [112], pp. 350–369
Bonissone, P.P., Eklund, N., Goebel, K.: Using an ensemble of classifiers to audit a production classifier. In: Oza, et al. [95], pp. 376–386
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L., Breiman, L.: Bagging predictors. Machine Learning, 123–140 (1996)
Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Boosting, bagging, and consensus based classification of multisource remote sensing data. In: Kittler, Roli [74], pp. 279–288
Bruzzone, L., Cossu, R.: A robust multiple classifier system for a partially unsupervised updating of land-cover maps. In: Kittler, Roli [74], pp. 259–268
Bruzzone, L., Cossu, R., Prieto, D.F.: Combining parametric and nonparametric classifiers for an unsupervised updating of land-cover maps. In: Kittler, Roli [73], pp. 290–299
Cappelli, R., Maio, D., Maltoni, D.: Combining fingerprint classifiers. In: Kittler, Roli [73], pp. 351–361
Chawla, N.V., Bowyer, K.W.: Designing multiple classifier systems for face recognition. In: Oza, et al. [95], pp. 407–416
Chindaro, S., Sirlantzis, K., Fairhurst, M.C.: Analysis and modelling of diversity contribution to ensemble-based texture recognition performance. In: Oza, et al. [95], pp. 387–396
Christensen, H.U., Arroyo, D.O.: Applying data fusion methods to passage retrieval in qas. In: Haindl, et al. [59], pp. 82–92
Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: A cascaded multiple expert system for verification. In: Kittler, Roli [73], pp. 330–339
Cordella, L.P., Limongiello, A., Sansone, C.: Network intrusion detection by a multi-stage classification system. In: Roli, et al. [109], pp. 324–333
Cordella, L.P., De Santo, M., Percannella, G., Sansone, C., Vento, M.: A multi-expert system for movie segmentation. In: Roli, Kittler [107], pp. 304–313
Csirik, J., Bertholet, P., Bunke, H.: Sequential classifier combination for pattern recognition in wireless sensor networks. In: Sansone, et al. [112], pp. 187–196
Dahmen, J., Keysers, D., Ney, H.: Combined classification of handwritten digits using the ’virtual test sample method’. In: Kittler, Roli [74], pp. 109–118
Dainotti, A., Pescapè, A., Sansone, C., Quintavalle, A.: Using a behaviour knowledge space approach for detecting unknown ip traffic flows. In: Sansone, et al. [112], pp. 360–369
de Borda, J.-C.: Memoire sur les elections au scrutin. Memoires de l’Academie Royale des Sciences, 657–664 (1781)
Degtyarev, N., Seredin, O.: A geometric approach to face detector combining. In: Sansone, et al. [112], pp. 299–308
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, Roli [73], pp. 1–15
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Dolenko, S.A., Orlov, Y.V., Persiantsev, I.G., Shugai, J.S., Dmitriev, A.V., Suvorova, A.V., Veselovsky, I.S.: Solar wind data analysis using self-organizing hierarchical neural network classifiers. In: Kittler, Roli [74], pp. 289–298
Du, P., Li, G., Zhang, W., Wang, X., Sun, H.: Consistency measure of multiple classifiers for land cover classification by remote sensing image. In: Benediktsson, et al. [12], pp. 398–407
Du, P., Sun, H., Zhang, W.: Target identification from high resolution remote sensing image by combining multiple classifiers. In: Benediktsson, et al. [12], pp. 408–417
Duin, R.P.W., Juszczak, P., de Ridder, D., Paclík, P., Pekalska, E., Tax, D.M.J.: PR-Tools 4.0, a Matlab toolbox for pattern recognition (2004), http://www.prtools.org
Ebrahimpour, R., Kabir, E., Yousefi, M.R.: View-based eigenspaces with mixture of experts for view-independent face recognition. In: Haindl, et al. [59], pp. 131–140
Erdogan, H., Erçil, A., Ekenel, H.K., Bilgin, S.Y., Eden, I., Kirisçi, M., Abut, H.: Multi-modal person recognition for vehicular applications. In: Oza, et al. [95], pp. 366–375
Fanelli, A.M., Castellano, G., Buscicchio, C.A.: A modular neuro-fuzzy network for musical instruments classification. In: Kittler, Roli [73], pp. 372–382
Fiérrez-Aguilar, J., Garcia-Romero, D., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Speaker verification using adapted user-dependent multilevel fusion. In: Oza, et al. [95], pp. 356–365
Foggia, P., Sansone, C., Tortorella, F., Vento, M.: Automatic classification of clustered microcalcifications by a multiple classifier system. In: Kittler, Roli [74], pp. 208–217
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann (1996)
Frinken, V., Fischer, A., Bunke, H.: Combining neural networks to improve performance of handwritten keyword spotting. In: Gayar, et al. [52], pp. 215–224
Fröba, B., Rothe, C., Küblbeck, C.: Statistical sensor calibration for fusion of different classifiers in a biometric person recognition framework. In: Kittler, Roli [73], pp. 362–371
Fröba, B., Zink, W.: On the combination of different template matching strategies for fast face detection. In: Kittler, Roli [74], pp. 418–428
El Gayar, N., Kittler, J., Roli, F. (eds.): MCS 2010. LNCS, vol. 5997. Springer, Heidelberg (2010)
Giacinto, G., Roli, F., Didaci, L.: A modular multiple classifier system for the detection of intrusions in computer networks. In: Windeatt, Roli [134], pp. 346–355
Gini, G.C., Lorenzini, M., Benfenati, E., Brambilla, R., Malvé, L.: Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromatic compounds. In: Kittler, Roli [74], pp. 126–135
Gordon, J., Shortliffe, E.H.: The dempster-shafer theory of evidence. In: Buchanan, B.G., Shortliffe, E.H. (eds.) Rule-Based Expert Systems, pp. 272–292. Addison Wesley Publishing Company, Reading (1984)
Günter, S., Bunke, H.: New boosting algorithms for classification problems with large number of classes applied to a handwritten word recognition task. In: Windeatt, Roli [134], pp. 326–335
Günter, S., Bunke, H.: Ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition. In: Roli, et al. [109], pp. 314–323
Hady, M.F.A., Schwenker, F.: Combining committee-based semi-supervised and active learning and its application to handwritten digits recognition. In: Gayar, et al. [52], pp. 225–234
Haindl, M., Kittler, J., Roli, F. (eds.): MCS 2007. LNCS, vol. 4472. Springer, Heidelberg (2007)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
Higgins, J.E., Dodd, T.J., Damper, R.I.: Application of multiple classifier techniques to subband speaker identification with an hmm/ann system. In: Kittler, Roli [74], pp. 369–377
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Huang, Y.S., Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 90–94 (1995)
Ianakiev, K.G., Govindaraju, V.: Architecture for classifier combination using entropy measures. In: Kittler, Roli [73], pp. 340–350
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Jaser, E., Kittler, J., Christmas, W.J.: Building classifier ensembles for automatic sports classification. In: Windeatt, Roli [134], pp. 366–374
Jiang, X., Yu, K., Bunke, H.: Classifier combination for grammar-guided sentence recognition. In: Kittler, Roli [73], pp. 383–392
Khademi, M., Shalmani, M.T.M., Kiapour, M.H., Kiaei, A.A.: Recognizing combinations of facial action units with different intensity using a mixture of hidden markov models and neural network. In: Gayar, et al. [52], pp. 304–313
Khreich, W., Granger, E., Miri, A., Sabourin, R.: Incremental boolean combination of classifiers. In: Sansone, et al. [112], pp. 340–349
Kittler, J., Ballette, M., Czyz, J., Roli, F., Vandendorpe, L.: Decision level fusion of intramodal personal identity verification experts. In: Roli, Kittler [107], pp. 314–324
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Kittler, J., Poh, N., Merati, A.: Cohort based approach to multiexpert class verification. In: Sansone, et al. [112], pp. 319–329
Kittler, J., Roli, F. (eds.): MCS 2000. LNCS, vol. 1857. Springer, Heidelberg (2000)
Kittler, J., Roli, F. (eds.): MCS 2001. LNCS, vol. 2096. Springer, Heidelberg (2001)
Kittler, J., Sadeghi, M.: Physics-based decorrelation of image data for decision level fusion in face verification. In: Roli, et al. [109], pp. 354–363
Ko, A.H.-R., Sabourin, R., de Souza Britto Jr., A.: A new hmm-based ensemble generation method for numeral recognition. In: Haindl, et al. [59], pp. 52–61
Kumar, S., Ghosh, J., Crawford, M.M.: A hierarchical multiclassifier system for hyperspectral data analysis. In: Kittler, Roli [73], pp. 270–279
Kumazawa, I.: Shape matching and extraction by an array of figure-and-ground classifiers. In: Kittler, Roli [73], pp. 393–402
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley Interscience (2004)
Lam, L.: Classifier combinations: Implementations and theoretical issues. In: Kittler, Roli [73], pp. 77–86
Di Lecce, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A.: A multi-expert system for dynamic signature verification. In: Kittler, Roli [73], pp. 320–329
Li, P., Chan, K.L., Fu, S., Krishnan, S.M.: An abnormal ecg beat detection approach for long-term monitoring of heart patients based on hybrid kernel machine ensemble. In: Oza, et al. [95], pp. 346–355
Lienemann, K., Plötz, T., Fink, G.A.: On the application of svm-ensembles based on adapted random subspace sampling for automatic classification of nmr data. In: Haindl, et al. [59], pp. 42–51
Lienemann, K., Plötz, T., Fink, G.A.: Stacking for ensembles of local experts in metabonomic applications. In: Benediktsson, et al. [12], pp. 498–508
Loog, M., Li, Y., Tax, D.M.J.: Maximum membership scale selection. In: Benediktsson, et al. [12], pp. 468–477
Lu, Y.: Knowledge integration in a multiple classifier system. Appl. Intell. 6(2), 75–86 (1996)
Marasco, E., Johnson, P., Sansone, C., Schuckers, S.: Increase the security of multibiometric systems by incorporating a spoofing detection algorithm in the fusion mechanism. In: Sansone, et al. [112], pp. 309–318
Marcialis, G.L., Roli, F.: High security fingerprint verification by perceptron-based fusion of multiple matchers. In: Roli, et al. [109], pp. 364–373
Marcialis, G.L., Roli, F.: Serial fusion of fingerprint and face matchers. In: Haindl, et al. [59], pp. 151–160
Masulli, F., Pardo, M., Sberveglieri, G., Valentini, G.: Boosting and classification of electronic nose data. In: Roli, Kittler [107], pp. 262–271
Melville, P., Mooney, R.J.: Diverse ensembles for active learning. In: Brodley, C.E. (ed.) ICML. ACM International Conference Proceeding Series, vol. 69. ACM (2004)
Merler, S., Furlanello, C., Larcher, B., Sboner, A.: Tuning cost-sensitive boosting and its application to melanoma diagnosis. In: Kittler, Roli [74], pp. 32–42
Minguillón, J., Tate, A.R., Arús, C., Griffiths, J.R.: Classifier combination for in vivo magnetic resonance spectra of brain tumours. In: Roli, Kittler [107], pp. 282–292
Mohamed, T.A., El Gayar, N., Atiya, A.F.: A co-training approach for time series prediction with missing data. In: Haindl et al. [59], pp. 93–102
Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.): MCS 2005. LNCS, vol. 3541. Springer, Heidelberg (2005)
Oza, N.C., Tumer, K., Tumer, I.Y., Huff, E.M.: Classification of aircraft maneuvers for fault detection. In: Windeatt, Roli [134], pp. 375–384
Powalka, R.K., Sherkat, N., Whitrow, R.J.: Multiple recognizer combination topologies. In: Simner, M.L., Leedham, C.G., Thomassen, A.J.W.M. (eds.) Handwriting and Drawing Research: Basic and Applied Issues. IOS Press (1995)
Prabhakar, S., Jain, A.K.: Decision-level fusion in fingerprint verification. In: Kittler, Roli [74], pp. 88–98
Pranckeviciene, E., Baumgartner, R., Somorjai, R.L.: Using domain knowledge for in the random subspace method: Application: Application to the classification of biomedical spectra. In: Oza, et al. [95], pp. 336–345
Procopio, M.J., Kegelmeyer, W.P., Grudic, G.Z., Mulligan, J.: Terrain segmentation with on-line mixtures of experts for autonomous robot navigation. In: Benediktsson, et al. [12], pp. 385–397
Rahman, F., Tarnikova, Y., Kumar, A., Alam, H.: Second guessing a commercial ’black box’ classifier by an ’in house’ classifier: Serial classifier combination in a speech recognition application. In: Roli, et al. [109], pp. 374–383
Rajan, S., Ghosh, J.: An empirical comparison of hierarchical vs. two-level approaches to multiclass problems. In: Roli, et al. [109], pp. 283–292
Rajan, S., Ghosh, J.: Exploiting class hierarchies for knowledge transfer in hyperspectral data. In: Oza, et al. [95], pp. 417–427
Raudys, S., Baykan, Ö.K., Babalik, A., Denisov, V., Bielskis, A.A.: Classifiers fusion in recognition of wheat varieties. In: Haindl, et al. [59], pp. 62–71
Re, M., Valentini, G.: Ensemble based data fusion for gene function prediction. In: Benediktsson, et al. [12], pp. 448–457
Re, M., Valentini, G.: An experimental comparison of hierarchical bayes and true path rule ensembles for protein function prediction. In: Gayar, et al. [52], pp. 294–303
Roli, F., Kittler, J. (eds.): MCS 2002. LNCS, vol. 2364. Springer, Heidelberg (2002)
Roli, F., Kittler, J., Fumera, G., Muntoni, D.: An experimental comparison of classifier fusion rules for multimodal personal identity verification systems. In: Roli, Kittler [107], pp. 325–336
Roli, F., Kittler, J., Windeatt, T. (eds.): MCS 2004. LNCS, vol. 3077. Springer, Heidelberg (2004)
Sadeghi, M., Khoshrou, S., Kittler, J.: Confidence based gating of colour features for face authentication. In: Haindl, et al. [59], pp. 121–130
Samadzadegan, F., Bigdeli, B., Ramzi, P.: A multiple classifier system for classification of lidar remote sensing data using multi-class svm. In: Gayar, et al. [52], pp. 254–263
Sansone, C., Kittler, J., Roli, F. (eds.): MCS 2011. LNCS, vol. 6713. Springer, Heidelberg (2011)
Sansone, C., Paduano, V., Ceccarelli, M.: Combining 2d and 3d features to classify protein mutants in hela cells. In: Gayar, et al. [52], pp. 284–293
De Santo, M., Percannella, G., Sansone, C., Vento, M.: Combining audio-based and video-based shot classification systems for news videos segmentation. In: Oza, et al. [95], pp. 397–406
Schettini, R., Brambilla, C., Cusano, C.: Content-based classification of digital photos. In: Roli, Kittler [107], pp. 272–281
Seewald, A.K.: How to make stacking better and faster while also taking care of an unknown weakness. In: Sammut, C., Hoffmann, A.G. (eds.) Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), University of New South Wales, Sydney, Australia, July 8-12, pp. 554–561. Morgan Kaufmann (2002)
Serrano, Á., de Diego, I.M., Conde, C., Cabello, E., Bai, L., Shen, L.: Fusion of support vector classifiers for parallel gabor methods applied to face verification. In: Haindl, et al. [59], pp. 141–150
Sirlantzis, K., Fairhurst, M.C., Hoque, S.: Genetic algorithms for multi-classifier system configuration: A case study in character recognition. In: Kittler, Roli [74], pp. 99–108
Sirlantzis, K., Hoque, S., Fairhurst, M.C.: Input space transformations for multi-classifier systems based on n-tuple classifiers with application to handwriting recognition. In: Windeatt, Roli [134], pp. 356–365
Slavík, P., Govindaraju, V.: Use of lexicon density in evaluating word recognizers. In: Kittler, Roli [73], pp. 310–319
Smits, P.C.: Combining supervised remote sensing image classifiers based on individual class performances. In: Kittler, Roli [74], pp. 269–278
Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, Roli [73], pp. 52–66
Sun, S.: Ensemble learning methods for classifying eeg signals. In: Haindl, et al. [59], pp. 113–120
Sun, S.: An improved random subspace method and its application to eeg signal classification. In: Haindl, et al. [59], pp. 103–112
Svetnik, V., Liaw, A., Tong, C., Wang, T.: Application of breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules. In: Roli, et al. [109], pp. 334–343
Ting, K.M., Zhu, L.: Boosting support vector machines successfully. In: Benediktsson, et al. [12], pp. 509–518
Tulyakov, S., Govindaraju, V.: Neural network optimization for combinations in identification systems. In: Benediktsson, et al. [12], pp. 418–427
Visentini, I., Kittler, J., Foresti, G.L.: Diversity-based classifier selection for adaptive object tracking. In: Benediktsson, et al. [12], pp. 438–447
Wan, W., Fraser, D.: A multiple self-organizing map scheme for remote sensing classification. In: Kittler, Roli [73], pp. 300–309
Wang, X., Tang, X.: Experimental study on multiple lda classifier combination for high dimensional data classification. In: Roli, et al. [109], pp. 344–353
Waske, B., Benediktsson, J.A., Sveinsson, J.R.: Classifying remote sensing data with support vector machines and imbalanced training data. In: Benediktsson, et al. [12], pp. 375–384
Webb, G.I.: Multiboosting: A technique for combining boosting and wagging. Machine Learning 40(2), 159–196 (2000)
Wilczok, E., Lellmann, W.: Design and evaluation of an adaptive combination framework for ocr result strings. In: Windeatt, Roli [134], pp. 395–404
Windeatt, T., Roli, F. (eds.): MCS 2003. LNCS, vol. 2709. Springer, Heidelberg (2003)
Windridge, D., Bowden, R.: Induced decision fusion in automated sign language interpretation: Using ica to isolate the underlying components of sign. In: Roli, et al. [109], pp. 303–313
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Witten, I.H., Frank, E., Hal, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)
Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)
Xu, J.-W., Singh, V., Govindaraju, V., Neogi, D.: A cascade multiple classifier system for document categorization. In: Benediktsson, et al. [12], pp. 458–467
Yousri, N.A.: A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression. In: Gayar, et al. [52], pp. 274–283
Zhang, C.-X., Duin, R.P.W.: An empirical study of a linear regression combiner on multi-class data sets. In: Benediktsson, et al. [12], pp. 478–487
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Gargiulo, F., Mazzariello, C., Sansone, C. (2013). Multiple Classifier Systems: Theory, Applications and Tools. In: Bianchini, M., Maggini, M., Jain, L. (eds) Handbook on Neural Information Processing. Intelligent Systems Reference Library, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36657-4_10
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