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  3. Machine Learning
  • Cited by 671
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    • Publisher:
      Cambridge University Press
      Publication date:
      November 2012
      September 2012
      ISBN:
      9780511973000
      9781107096394
      9781107422223
      DOI:
      https://doi.org/10.1017/CBO9780511973000
      Dimensions:
      (246 x 189 mm)
      Weight & Pages:
      1.04kg, 410 Pages
      Dimensions:
      (246 x 189 mm)
      Weight & Pages:
      0.88kg, 409 Pages
      • Subjects:
      Communications and Signal Processing, Engineering, Computer Science, Pattern Recognition and Machine Learning
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    Subjects:
    Communications and Signal Processing, Engineering, Computer Science, Pattern Recognition and Machine Learning
    Information

    Book description

    As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    Reviews

    "This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms."< /br>Fernando Berzal, Computing Reviews

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    Contents

    Contents
    References
    References
    References
    Abudawood, T. (2011). Multi-class subgroup discovery: Heuristics, algorithms and predictiveness. Ph.D. thesis, University of Bristol, Department of Computer Science, Faculty of Engineering. 357
    Abudawood, T. and Flach, P.A. (2009). Evaluation measures for multi-class subgroup discovery. In W.L., Buntine, M., Grobelnik, D., Mladenić and J., Shawe-Taylor (eds.), Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2009), Part I, LNCS, volume 5781, pp. 35–50. Springer. 193
    Agrawal, R., Imielinski, T. and Swami, A.N. (1993). Mining association rules between sets of items in large databases. In P., Buneman and S., Jajodia (eds.), Proceedings of the ACM International Conference on Management of Data (SIGMOD 1993), pp. 207–216. ACM Press. 103
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. and Verkamo, A.I. (1996). Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press. 193
    Allwein, E.L., Schapire, R.E. and Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. In P., Langley (ed.), Proceedings of the Seventeenth In ternational Conference on Machine Learning (ICML 2000), pp. 9–16. Morgan Kaufmann. 102
    Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Computation 9(7):1545–1588.
    Angluin, D., Frazier, M. and Pitt, L. (1992). Learning conjunctions of Horn clauses. Machine Learning 9:147–164. 128
    Bakir, G., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B. and Vishwanathan, S.V.N. (2007). Predicting Structured Data. MIT Press. 361
    Banerji, R.B. (1980). Artificial Intelligence: A Theoretical Approach. Elsevier Science. 127
    Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1–127. 361
    Best, M.J. and Chakravarti, N. (1990). Active set algorithms for isotonic regression; a unifying framework. Mathematical Programming 47(1):425–439. 80, 229
    Blockeel, H. (2010 a). Hypothesis language. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 507–511. Springer. 127
    Blockeel, H. (2010 b). Hypothesis space. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 511–513. Springer. 127
    Blockeel, H., De Raedt, L. and Ramon, J. (1998). Top-down induction of clustering trees. In J.W., Shavlik (ed.), Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), pp. 55–63. Morgan Kaufmann. 103, 156
    Blumer, A., Ehrenfeucht, A., Haussler, D. and Warmuth, M.K. (1989). Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM 36(4):929–965. 128
    Boser, B.E., Guyon, I. and Vapnik, V. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the International Conference on Computational Learning Theor y (COLT 1992), pp. 144–152. 229
    Bouckaert, R. and Frank, E. (2004). Evaluating the replicability of significance tests for comparing learning algorithms. In H., Dai, R., Srikant and C., Zhang (eds.), Advances in Knowledge Discovery and Data Mining, LNCS, volume 3056, pp. 3–12. Springer. 358
    Boullé, M. (2004). Khiops: A statistical discretization method of continuous attributes. Machine Learning 55(1):53–69. 328
    Boullé, M. (2006). MODL: A Bayes optimal discretization method for continuous attributes. Machine Learning 65(1):131–165. 328
    Bourke, C., Deng, K., Scott, S.D., Schapire, R.E. and Vinodchandran, N.V. (2008). On reoptimizing multi-class classifiers. Machine Learning 71(2-3):219–242. 102
    Brazdil, P., Giraud-Carrier, C.G., Soares, C. and Vilalta, R. (2009). Metalearning – Applications to Data Mining. Springer. 342
    Brazdil, P., Vilalta, R., Giraud-Carrier, C.G. and Soares, C. (2010). Metalearning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 662–666. Springer. 342
    Breiman, L. (1996 a). Bagging predictors. Machine Learning 24(2):123–140. 341
    Breiman, L. (1996 b). Stacked regressions. Machine Learning 24(1):49–64. 342
    Breiman, L. (2001). Random forests. Machine Learning 45(1):5–32. 341
    Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. 156
    Brier, G.W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review 78(1):1–3. 80
    Brown, G. (2010). Ensemble learning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 312–320. Springer. 341
    Bruner, J.S., Goodnow, J.J. and Austin, G.A. (1956). A Study of Thinking. Science Editions. 2nd edn 1986. 127
    Cesa-Bianchi, N. and Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press. 361
    Cestnik, B. (1990). Estimating probabilities: A crucial task in machine learning. In Proceedings of the European Conference on Artificial Intelligence (ECAI 1990), pp. 147–149. 296
    Clark, P. and Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Y., Kodratoff (ed.), Proceedings of the European Working Session on Learning (EWSL 1991), LNCS, volume 482, pp. 151–163. Springer. 192
    Clark, P. and Niblett, T. (1989). The CN2 induction algorithm. Machine Learning 3:261–283. 192
    Cohen, W. W. (1995). Fast effective rule induction. In A., Prieditis and S.J., Russell (eds.), Proceedings of the Twelfth International Conference on Machine Learning (ICML 1995), pp. 115–123. Morgan Kaufmann. 192, 341
    Cohen, W.W. and Singer, Y. (1999). A simple, fast, and effictive rule learner. In J., Hendler and D., Subramanian (eds.), Proceedings of the Sixteenth National Conference on Ar tificial Intelligence (AAAI 1999), pp. 335–342. AAAI Press / MIT Press. 341
    Cohn, D. (2010). Active learning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 10–14. Springer. 128
    Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning 20(3):273–297. 229
    Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1):21–27. 260
    Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge University Press. 229
    Dasgupta, S. (2010). Active learning theory. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 14–19. Springer. 128
    Davis, J. and Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In W.W., Cohen and A., Moore (eds.), Proceedings of the Twenty-Third International Conference on Machine Learning (ICML 2006), pp. 233–240. ACM Press. 358
    De Raedt, L. (1997). Logical settings for concept-learning. Artificial Intelligence 95(1):187–201. 128
    De Raedt, L. (2008). Logical and Relational Learning. Springer. 193
    De Raedt, L. (2010). Logic of generality. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 624–631. Springer. 128
    De Raedt, L. and Kersting, K. (2010). Statistical relational learning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 916–924. Springer. 193
    Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) pp. 1–38. 296
    Demšar, J. (2008). On the appropriateness of statistical tests in machine learning. In Proceedings of the ICML'08 Workshop on Evaluation Methods for Machine Learning. 359
    Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7:1–30. 359
    Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10(7):1895–1923. 358
    Dietterich, T.G. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2:263–286. 102
    Dietterich, T. G., Kearns, M.J. and Mansour, Y. (1996). Applying the weak learning framework to understand and improve c4.5. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 96–104. 156
    Ding, C.H.Q. and He, X. (2004). K-means clustering via principal component analysis. In C.E., Brodley (ed.), Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004). ACM Press. 329
    Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29(2):103–130. 296
    Donoho, S.K. and Rendell, L.A. (1995). Rerepresenting and restructuring domain theories: A constructive induction approach. Journal of Artificial Intelligence Research 2:411–446. 328
    Drummond, C. (2006). Machine learning as an experimental science (revisited). In Proceedings of the AAAI'06 Workshop on Evaluation Methods for Machine Learning. 359
    Drummond, C. and Holte, R.C. (2000). Exploiting the cost (in)sensitivity of decision tree splitting criteria. In P., Langley (ed.), Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp. 239–246. Morgan Kaufmann. 156
    Egan, J.P. (1975). Signal Detection Theory and ROC Analysis. Academic Press. 80
    Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters 27(8):861–874. 80, 358
    Fawcett, T. and Niculescu-Mizil, A. (2007). PAV and the ROC convex hull. Machine Learning 68(1):97–106. 80, 229
    Fayyad, U.M. and Irani, K.B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1993), pp. 1022–1029. 328
    Ferri, C., Flach, P.A. and Hernández-Orallo, J. (2002). Learning decision trees using the area under the ROC curve. In C., Sammut and A.G., Hoffmann (eds.), Proceedings of the Ni neteenth International Conference on Machine Learning (ICML 2002), pp. 139–146. Morgan Kaufmann. 156
    Ferri, C., Flach, P.A. and Hernández-Orallo, J. (2003). Improving the AUC of probabilistic estimation trees. In N., Lavrač, D., Gamberger, L., Todorovski and H., Blockeel (eds.), Proceedings of the European Conference on Machine Learning (ECML 2003), LNCS, volume 2837, pp. 121–132. Springer. 156
    Fix, E. and Hodges, J.L. (1951). Discriminatory analysis. Nonparametric discrimination: Consistency properties. Technical report, USAF School of Aviation Medicine, Texas: Randolph Field. Report Number 4, Project Number 21-49-004. 260
    Flach, P.A. (1994). Simply Logical – Intelligent Reasoning by Example. Wiley. 193
    Flach, P.A. (2003). The geometry of ROC space: Understanding machine learning metrics through ROC isometrics. In T., Fawcett and N., Mishra (eds.), Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 194–201. AAAI Press. 156
    Flach, P.A. (2010 a). First-order logic. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 410–415. Springer. 128
    Flach, P.A. (2010 b). ROC analysis. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 869–875. Springer. 80
    Flach, P.A. and Lachiche, N. (2001). Confirmation-guided discovery of first-order rules with Tertius. Machine Learning 42(1/2):61–95. 193
    Flach, P.A. and Matsubara, E.T. (2007). A simple lexicographic ranker and probability estimator. In J.N., Kok, J., Koronacki, R.L., de Mántaras, S., Matwin, D., Mladenic and A., Skowron (eds.), Proceedings of the Eighteenth European Conference on Machine Learning (ECML 2007), LNCS, volume 4701, pp. 575–582. Springer. 80, 229
    Freund, Y., Iyer, R.D., Schapire, R.E. and Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4:933–969. 341
    Freund, Y. and Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1):119–139. 341
    Fürnkranz, J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review 13(1):3–54. 192
    Fürnkranz, J. (2010). Rule learning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 875–879. Springer. 192
    Fürnkranz, J. and Flach, P.A. (2003). An analysis of rule evaluation metrics. In T., Fawcett and N., Mishra (eds.), Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 202–209. AAAI Press. 79
    Fürnkranz, J. and Flach, P.A. (2005). ROC ‘n’ Rule learning – towards a better understanding of covering algorithms. Machine Learning 58(1):39–77. 192
    Fürnkranz, J., Gamberger, D. and Lavrač, N. (2012). Foundations of Rule Learning. Springer. 192
    Fürnkranz, J. and Hüllermeier, E. (eds.) (2010). Preference Learning. Springer. 361
    Fürnkranz, J. and Widmer, G. (1994). Incremental reduced error pruning. In Proceedings of the Eleventh International Conference on Machine Learning (ICML 1994), pp. 70–77. 192
    Gama, J. and Gaber, M.M. (eds.) (2007). Learning from Data Streams: Processing Techniques in Sensor Networks. Springer. 361
    Ganter, B. and Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations. Springer. 127
    Garriga, G.C., Kralj, P. and Lavrač, N. (2008). Closed sets for labeled data. Journal of Machine Learning Research 9:559–580. 127
    Gärtner, T. (2009). Kernels for Structured Data. World Scientific. 230
    Grünwald, P.D. (2007). The Minimum Description Length Principle. MIT Press. 297
    Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research 3:1157–1182. 328
    Hall, M.A. (1999). Correlation-based feature selection for machine learning. Ph.D. thesis, University of Waikato. 328
    Han, J., Cheng, H., Xin, D. and Yan, X. (2007). Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery 15(1):55–86. 193
    Hand, D.J. and Till, R.J. (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45(2):171–186. 102
    Haussler, D. (1988). Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence 36(2):177–221. 128
    Hernández-Orallo, J., Flach, P.A. and Ferri, C. (2011). Threshold choice methods: The missing link. Available online at http://arxiv.org/abs/1112.2640. 358
    Ho, T.K. (1995). Random decision forests. In Proceedings of the International Conference on Document Analysis and Recognition, p. 278. IEEE Computer Society, Los Alamitos, CA, USA. 341
    Hoerl, A.E. and Kennard, R.W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics pp. 55–67. 228
    Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the Twenty-Second Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR 1999), pp. 50–57. ACM Press. 329
    Hunt, E.B., Marin, J. and Stone, P.J. (1966). Experiments in Induction. Academic Press. 127, 156
    Jain, A.K., Murty, M.N. and Flynn, P.J. (1999). Data clustering: A review. ACM Computing Surveys 31(3):264–323. 261
    Japkowicz, N. and Shah, M. (2011). Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press. 357
    Jebara, T. (2004). Machine Learning: Discriminative and Generative. Springer. 296
    John, G.H. and Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI 1995), pp. 338–345. Morgan Kaufmann. 295
    Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley. 261
    Kearns, M.J. and Valiant, L.G. (1989). Cryptographic limitations on learning Boolean formulae and finite automata. In D.S., Johnson (ed.), Proceedings of the Twenty-First Annual ACM Symposium on Theory of Computing (STOC 1989), pp. 433–444. ACM Press. 341
    Kearns, M.J. and Valiant, L.G. (1994). Cryptographic limitations on learning Boolean formulae and finite automata. Journal of the ACM 41(1):67–95. 341
    Kerber, R. (1992). Chimerge: Discretization of numeric attributes. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI 1992), pp. 123–128. AAAI Press. 328
    Kibler, D.F. and Langley, P. (1988). Machine learning as an experimental science. In Proceedings of the European Working Session on Learning (EWSL 1988), pp. 81–92. 359
    King, R.D., Srinivasan, A. and Dehaspe, L. (2001). Warmr: A data mining tool for chemical data. Journal of Computer-Aided Molecular Design 15(2):173–181. 193
    Kira, K. and Rendell, L.A. (1992). The feature selection problem: Traditional methods and a new algorithm. In W.R., Swartout (ed.), Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI 1992), pp. 129–134. AAAI Press / MIT Press. 328
    Klösgen, W. (1996). Explora: A multipattern and multistrategy discovery assistant. In Advances in Knowledge Discovery and Data Mining, pp. 249–271. MIT Press. 103
    Kohavi, R. and John, G.H. (1997). Wrappers for feature subset selection. Artificial Intelligence 97(1-2):273–324. 328
    Koren, Y., Bell, R. and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. IEEE Computer 42(8):30–37. 328
    Kramer, S. (1996). Structural regression trees. In Proceedings of the National Conference on Artificial Intelligence (AAAI 1996), pp. 812–819. 156
    Kramer, S., Lavrač, N. and Flach, P.A. (2000). Propositionalization approaches to relational data mining. In S., Džeroski and N., Lavrač (eds.), Relational Data Mining, pp. 262–286. Springer. 328
    Krogel, M.A., Rawles, S., Zelezný, F., Flach, P.A., Lavrač, N. and Wrobel, S. (2003). Comparative evaluation of approaches to propositionalization. In T., Horváth (ed.), Proceedings of the Thir teenth International Conference on Inductive Logic Programming (ILP 2003), LNCS, volume 2835, pp. 197–214. Springer. 328
    Kuncheva, L.I. (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons. 341
    Lachiche, N. (2010). Propositionalization. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 812–817. Springer. 328
    Lachiche, N. and Flach, P.A. (2003). Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In T., Fawcett and N., Mishra (eds.), Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 416–423. AAAI Press. 102
    Lafferty, J.D., McCallum, A. and Pereira, F.C.N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In C.E., Brodley and A.P., Danyluk (eds.), Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), pp. 282–289. Morgan Kaufmann. 296
    Langley, P. (1988). Machine learning as an experimental science. Machine Learning 3:5–8. 359
    Langley, P. (1994). Elements of Machine Learning. Morgan Kaufmann. 156
    Langley, P. (2011). The changing science of machine learning. Machine Learning 82(3):275–279. 359
    Lavrač, N., Kavšek, B., Flach, P.A. and Todorovski, L. (2004). Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5:153–188. 193
    Lee, D.D., Seung, H.S. et al. (1999). Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791. 328
    Leman, D., Feelders, A. and Knobbe, A.J. (2008). Exceptional model mining. In W., Daelemans, B., Goethals and K., Morik (eds.), Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD 2008), Part II, LNCS, volume 5212, pp. 1–16. Springer. 103
    Lewis, D. (1998). Naive Bayes at forty: The independence assumption in information retrieval. In Proceedings of the Tenth European Conference on Machine Learning (ECML 1998), pp. 4–15. Springer. 295
    Li, W., Han, J. and Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In N., Cercone, T.Y., Lin and X., Wu (eds.), Proceedings of the IEEE International Conference on Data Mining (ICDM 2001), pp. 369–376. IEEE Computer Society. 193
    Little, R.J.A. and Rubin, D.B. (1987). Statistical Analysis with Missing Data. Wiley. 296
    Liu, B., Hsu, W. and Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the Fourth In ternational Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 80–86. AAAI Press. 193
    Lloyd, J.W. (2003). Logic for Learning – Learning Comprehensible Theories from Structured Data. Springer. 193
    Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2):129–137. 261
    Mahalanobis, P.C. (1936). On the generalised distance in statistics. Proceedings of the National Institute of Science, India 2(1):49–55. 260
    Mahoney, M.W. and Drineas, P. (2009). CUR matrix decompositions for improved data analysis. Proceedings of the National Academy of Sciences 106(3):697. 329
    McCallum, A. and Nigam, K. (1998). A comparison of event models for naive Bayes text classification. In Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, pp. 41–48. 295
    Michalski, R.S. (1973). Discovering classification rules using variable-valued logic system VL1. In Proceedings of the Third International Joint Conference on Artificial Intelligence, pp. 162–172. Morgan Kaufmann Publishers. 127
    Michalski, R.S. (1975). Synthesis of optimal and quasi-optimal variable-valued logic formulas. In Proceedings of the 1975 International Symposium on Multiple-Valued Logic, pp. 76–87. 192
    Michie, D., Spiegelhalter, D.J. and Taylor, C.C. (1994). Machine Learning, Neural and Statistical Classification. Ellis Horwood. 342
    Miettinen, P. (2009). Matrix decomposition methods for data mining: Computational complexity and algorithms. Ph.D. thesis, University of Helsinki. 329
    Minsky, M. and Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry. MIT Press. 228
    Mitchell, T.M. (1977). Version spaces: A candidate elimination approach to rule learning. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 305–310. Morgan Kaufmann Publishers. 127
    Mitchell, T.M. (1997). Machine Learning. McGraw-Hill. 128
    Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing 13(3&4):245–286. 193
    Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P.A., Inoue, K. and Srinivasan, A. (2012). ILP turns 20 – biography and future challenges. Machine Learning 86(1):3–23. 193
    Muggleton, S. and Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the International Conference on Algorithmic Learning Theory (ALT 1990), pp. 368–381. 193
    Murphy, A.H. and Winkler, R.L. (1984). Probability forecasting in meteorology. Journal of the American Statistical Association pp. 489–500. 80
    Nelder, J.A. and Wedderburn, R.W.M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A (General) pp. 370–384. 296
    Novikoff, A.B. (1962). On convergence proofs on perceptrons. In Proceedings of the Symposium on the Mathematical Theory of Automata, volume 12, pp. 615–622. Polytechnic Institute of Brooklyn, New York. 228
    Pasquier, N., Bastide, Y., Taouil, R. and Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In Proceedings of the In ternational Conference on Database Theory (ICDT 1999), pp. 398–416. Springer. 127
    Peng, Y., Flach, P.A., Soares, C. and Brazdil, P. (2002). Improved dataset characterisation for meta-learning. In S., Lange, K., Satoh and C.H., Smith (eds.), Proceedings of the Fifth International Conference on Discovery Science (DS 2002), LNCS, volume 2534, pp. 141–152. Springer. 342
    Pfahringer, B., Bensusan, H. and Giraud-Carrier, C.G. (2000). Meta-learning by land-marking various learning algorithms. In P., Langley (ed.), Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp. 743–750. Morgan Kaufmann. 342
    Platt, J.C. (1998). Using analytic QP and sparseness to speed training of support vector machines. In M.J., Kearns, S.A., Solla and D.A., Cohn (eds.), Advances in Neural Information Processing Systems 11 (NIPS 1998), pp. 557–563. MIT Press. 229
    Plotkin, G.D. (1971). Automatic methods of inductive inference. Ph.D. thesis, University of Edinburgh. 127
    Provost, F.J. and Domingos, P. (2003). Tree induction for probability-based ranking. Machine Learning 52(3):199–215. 156
    Provost, F.J. and Fawcett, T. (2001). Robust classification for imprecise environments. Machine Learning 42(3):203–231. 79
    Quinlan, J.R. (1986). Induction of decision trees. Machine Learning 1(1):81–106. 155
    Quinlan, J.R. (1990). Learning logical definitions from relations. Machine Learning 5:239–266. 193
    Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann. 156
    Ragavan, H. and Rendell, L.A. (1993). Lookahead feature construction for learning hard concepts. In Proceedings of the Tenth International Conference on Machine Learning (ICML 1993), pp. 252–259. Morgan Kaufmann. 328
    Rajnarayan, D.G. and Wolpert, D. (2010). Bias-variance trade-offs: Novel applications. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 101–110. Springer. 103
    Rissanen, J. (1978). Modeling by shortest data description. Automatica 14(5):465–471. 297
    Rivest, R.L. (1987). Learning decision lists. Machine Learning 2(3):229–246. 192
    Robnik-Sikonja, M. and Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53(1-2):23–69. 328
    Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6):386. 228
    Rousseeuw, P.J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20(0):53–65. 261
    Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning representations by back-propagating errors. Nature 323(6088):533–536. 229
    Schapire, R.E. (1990). The strength of weak learnability. Machine Learning 5:197–227. 341
    Schapire, R.E. (2003). The boosting approach to machine learning: An overview. In Nonlinear Estimation and Classification, pp. 149–172. Springer. 341
    Schapire, R.E., Freund, Y., Bartlett, P. and Lee, W.S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics 26(5):1651–1686. 341
    Schapire, R.E. and Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3):297–336. 341
    Settles, B. (2011). Active Learning. Morgan & Claypool. 361
    Shawe-Taylor, J. and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press. 230
    Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A. and Blake, A. (2011). Real-time human pose recognition in parts from single depth images. In Proceedings of the Twenty-Four th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 1297–1304. 155
    Silver, D. and Bennett, K. (2008). Guest editor's introduction: special issue on inductive transfer learning. Machine Learning 73(3):215–220. 361
    Solomonoff, R.J. (1964 a). A formal theory of inductive inference: Part I. Information and Control 7(1):1–22. 297
    Solomonoff, R.J. (1964 b). A formal theory of inductive inference: Part II. Information and Control 7(2):224–254. 297
    Srinivasan, A. (2007). The Aleph manual, version 4 and above. Available online at www.cs.ox.ac.uk/activities/machlearn/Aleph/. 193
    Stevens, S.S. (1946). On the theory of scales of measurement. Science 103(2684):677–680. 327
    Sutton, R.S. and Barto, A.G. (1998). Reinforcement Learning: An Introduction. MIT Press. 361
    Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B (Methodological) pp. 267–288. 228
    Todorovski, L. and Dzeroski, S. (2003). Combining classifiers with meta decision trees. Machine Learning 50(3):223–249. 342
    Tsoumakas, G., Zhang, M.L. and Zhou, Z.H. (2012). Introduction to the special issue on learning from multi-label data. Machine Learning 88(1-2):1–4. 361
    Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley. 103
    Valiant, L.G. (1984). A theory of the learnable. Communications of the ACM 27(11):1134–1142. 128
    Vapnik, V.N. and Chervonenkis, A.Y. (1971). On uniform convergence of the frequencies of events to their probabilities. Teoriya Veroyatnostei I Ee Primeneniya 16(2):264–279. 128
    Vere, S.A. (1975). Induction of concepts in the predicate calculus. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence, pp. 281–287. 127
    von Hippel, P.T. (2005). Mean, median, and skew: Correcting a textbook rule. Journal of Statistics Education 13(2). 327
    Wallace, C.S. and Boulton, D.M. (1968). An information measure for classification. Computer Journal 11(2):185–194. 297
    Webb, G.I. (1995). Opus: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research 3:431–465. 192
    Webb, G.I., Boughton, J.R. and Wang, Z. (2005). Not so naive Bayes: Aggregating one-dependence estimators. Machine Learning 58(1):5–24. 295
    Winston, P.H. (1970). Learning structural descriptions from examples. Technical report, MIT Artificial Intelligence Lab. AITR-231. 127
    Wojtusiak, J., Michalski, R.S., Kaufman, K.A. and Pietrzykowski, J. (2006). The AQ21 natural induction program for pattern discovery: Initial version and its novel features. In Proceedings of the Eighteenth IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), pp. 523–526. 192
    Wolpert, D.H. (1992). Stacked generalization. Neural Networks 5(2):241–259. 342
    Zadrozny, B. and Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the Eighth ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD 2002), pp. 694–699. ACM Press. 80, 229
    Zeugmann, T. (2010). PAC learning. In C., Sammut and G.I., Webb (eds.), Encyclopedia of Machine Learning, pp. 745–753. Springer. 128
    Zhou, Z.H. (2012). Ensemble Me thods: Foundations and Algorithms. Taylor & Francis. 341

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