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Supplementary MaterialTowards Automatic Classification of NeuronsRubén Arma?anzas and Giorgio A. AscoliKrasnow Institute for Advanced Study, George Mason UniversityFairfax, VA 22030, USACorrespondence: ascoli@gmu.eduFigure S1. Major classification approaches with representative algorithms. References and links to available resources are provided in Table S1.Family/MethodRefsSoftwareProbabilisticBayesian Network Classifiers (BNCs)[S1]Weka, R(bnclassify), PyMVPABayesian Networks[S2]Weka, GeNIe, Bayesia, Hugin, R(bnlearn, deal)Expectation Maximization (EM)[S3]Weka, R(mclust)Generative Mixture Models[S4]Mixmod, R(Rmixmod)Factor Analysis for Biclustering[S5]R(fabia)Math FunctionsLogistic Regression[S6]Weka, R(glm2), Matlab(Statistics), PyMVPASupport Vector Machines[S7]Weka, Shogun, R(libsvm), Matlab(Statistics), PyMVPAArtificial Neural Networks[S8]Weka, Matlab(nnet, neuralnet)Kernel Methods[S9]Matlab(Statistics), R(stats, ks), PyMVPASemi-Supervised SVM (S3VM)[S10]SVMlight, UniverSVMSelf-Organizing Map (SOM)[S11]Matlab(SOM), R(som, kohonen)Subspace Clustering[S12]Weka(OpenSubspace), R(orclus, FisherEM)Principal Component Analysis[S13]Matlab(Statistics), R(stats)Distance-basedk-Nearest Neighbors[S14]Weka, Matlab(Statistics), R(knn), PyMVPAHierarchical Clustering[S15]Weka, Matlab(Statistics), R(stats, fastcluster)k-Means Clustering[S16]Weka, R(stats), Matlab(Statistics)k-Medians Clustering[S17]Weka, R(flexclust), Matlab(Statistics)SimilarityGraph-based[S18]R(spa), SemiLGaussian processes[S19]Weka, R(gptk), Matlab(GPML)Affinity Propagation[S20]Weka (APCluster), Matlab(apcluster), R(APCluster)Variance Biclustering[S21]R(biclust), Matlab(MTBA)RulesRIPPER[S22]Weka, R(caret), MLC++PRISM[S23]WekaTreesID3[S24]Weka, R(tree), Matlab(Statistics)C4.5[S25]Weka, R(tree), Matlab(Statistics)Random Forest[S26]Weka, R(randomForest), Matlab(randomforest-matlab)Wrapper approachesSelf-training[S27]R(DMwR)Co-training[S28]CoAL, Weka(collective-classification)Table S1. Major classification algorithms from a subset of representative families with original references and examples of implementations. Names in parentheses refer to particular packages that need to be installed. If none are noted, the algorithm is included within the basic installation.Software listNon-comprehensive exemplars of available programs implementing the algorithms presented in Table S1.Bayesia[S1] Friedman, N. et al. (1997) Bayesian network classifiers. Mach. Learn. 29, 131–163[S2] Perl, J. (1988) Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann[S3] Dempster, A.P. et al. (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm. J. R. Stat. Soc. Series B 39, 1–38[S4] Baluja, S. (1998) Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data. In Advances in Neural Information Processing Systems 11 (Kearns, M.J., Solla, S.A., Cohn, D.A., eds), pp. 854–860, MIT Press[S5] Hochreiter, S. et al. (2010) FABIA: Factor analysis for bicluster acquisition. Bioinformatics 26, 1520–1527[S6] Verhulst, P-F. (1845) Recherches mathématiques sur la loi d’accroissement de la population. In Nouveaux Mémoires de l’Académie Royale des Sciences, des Lettres et des Beaux-Arts de Belgique 20 (Hayez, M. eds), pp. 1–32, L’Académie Royale[S7] Cristianini, N. and Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press[S8] Werbos, P.J. (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Harvard University[S9] Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis, Chapman and Hall[S10] Bennett, K.P. and Demiriz, A. (1998) Semi-Supervised Support Vector Machines. In Advances in Neural Information Processing Systems 11 (Kearns, M.J. et al., eds), pp. 368–374, MIT Press [S11] Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69[S12] Agrawal, R. et al. (1998) Automatic subspace clustering of high dimensional data for data mining applications. In ACM SIGMOD international conference on management of data (Tiwary, A. and Franklin, M., eds.), pp. 94–105, ACM[S13] Pearson, K. (1901) On Lines and Planes of Closest Fit to Systems of Points in Space. Philos. Mag. 2, 559–572[S14] Cover, T. and Hart, P. (1967) Nearest neighbor pattern classification. IEEE T. Inform. Theory 13, 21–27[S15] van Rijsbergen, C.J. (1975) Information Retrieval. Butterworths[S16] Forgy, E.W. (1965) Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768–769[S17] Jain, A.K. and Dubes, R.C. (1988) Algorithms for Clustering Data. Prentice-Hall[S18] Zhu, X. et al. (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In International Conference in Machine Learning 3 (Fawcett, T. and Mishra, N., eds.), pp. 912–919, AAAI Press[S19] Rasmussen, C.E. and Williams, C.K.I. (2006) Gaussian Processes for Machine Learning. The MIT Press[S20] Frey, B.J. and Dueck, D. (2007) Clustering by Passing Messages Between Data Points. Science 315, 972–976[S21] Cheng, Y. and Church, G.M. (2000) Biclustering of expression data. In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (Altman, R. et al., eds.), pp. 93–103, AAAI Press[S22] Cohen, W.W. (1995) Fast Effective Rule Induction. In Proceedings of the Twelfth International Conference on Machine Learning ML95 (Prieditis, A. and Russell, S., eds.), pp. 115–123, Morgan Kaufmann[S23] Cendrowska, J. (1987) PRISM: An algorithm for inducing modular rules. Int. J. Man. Mach. Stud. 27, 349–370[S24] Quinlan, J.R. (1986) Induction of Decision Trees. Mach. Learn. 1, 81–106[S25] Quinlan, R. and Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann[S26] Breiman, L. (2001) Random Forests. Mach. Learn. 45, 5–32[S27] Scudder, H.J. (1965) Probability of error of some adaptive pattern-recognition machines. IEEE T. Inform. Theory 11, 363–371[S28] Blum, A. and Mitchell, T. (1998) Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (Bartlett, P. and Mansour, Y., eds.), pp. 92–100, ACM Press ................
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