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A Brief Review on Machine Learning

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Machine Learning

Abstract

The area of Machine Learning (ML) is interested in answering how a computer can “learn” specific tasks such as recognize characters, support the diagnosis of people under severe diseases, classify wine types, separate some material according to its quality (e.g. wood could be separated according to its weakness, so it could be later used to build either pencils or houses).

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Notes

  1. 1.

    Complete source-codes are available at the repository https://github.com/maponti/ml_statistical_learning. For more information, we suggest the reader to study the R manuals available at https://www.r-project.org.

  2. 2.

    Observe one can still define another function instead of using those.

  3. 3.

    When this algorithm employs more than a single hyperplane, otherwise it is the same as the Perceptron.

  4. 4.

    The term neuron is used due to the biological neuron motivation.

  5. 5.

    The reader may not confuse this bias term with the Bias-Variance Dilemma. The bias term θ is simply a space interception value.

  6. 6.

    We suggest to execute this function several times, in order to see the effects of using different starting random values for weight w 1 and θ.

  7. 7.

    The book Convex Optimization [2] is suggested.

  8. 8.

    We suggest the book Convex Optimization [2] for further references and to complement studies.

  9. 9.

    Constant c is the expected class and it is in range [0, 1].

  10. 10.

    We can take advantage of the Statistical Learning Theory to set an adequate number of hyperplanes depending on the target problem. This is discussed in Chap. 2.

  11. 11.

    Download the Iris dataset from the UCI Machine Learning Repository available at archive.ics.uci.edu/ml.

  12. 12.

    Download the Wine dataset from the UCI Machine Learning Repository available at archive.ics.uci.edu/ml.

  13. 13.

    The MNIST database is available at http://yann.lecun.com/exdb/mnist.

References

  1. C.M. Bishop, Pattern Recognition and Machine Learning. Information Science and Statistics (Springer, New York, 2006)

    MATH  Google Scholar 

  2. S. Boyd. L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004)

    Google Scholar 

  3. G. Carlsson, F. Mémoli, Characterization, stability and convergence of hierarchical clustering methods. J. Mach. Learn. Res. 11, 1425–1470 (2010)

    MathSciNet  MATH  Google Scholar 

  4. M.E. Celebi, K. Aydin, Unsupervised Learning Algorithms (Springer International Publishing, Berlin, 2016)

    Book  Google Scholar 

  5. C.M. Grinstead, L.J. Snell, Grinstead and Snell’s Introduction to Probability (American Mathematical Society, Providence, 2006), Version dated 4 July 2006 edition

    Google Scholar 

  6. S. Haykin, Neural Networks: A Comprehensive Foundation, 3rd edn. (Prentice-Hall, Upper Saddle River, 2007)

    MATH  Google Scholar 

  7. Y. LeCun, C. Cortes, MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/

  8. T.M. Mitchell, Machine Learning, 1st edn. (McGraw-Hill, New York, 1997)

    MATH  Google Scholar 

  9. H.-L. Nguyen, Y.-K. Woon, W.-K. Ng, A survey on data stream clustering and classification. Knowl. Inf. Syst. 45(3), 535–569 (2015)

    Article  Google Scholar 

  10. R Development Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2008). ISBN 3-900051-07-0

    Google Scholar 

  11. F. Rosenblatt, The perceptron: a perceiving and recognizing automaton, Technical report 85-460-1, Cornell Aeronautical Laboratory, 1957

    Google Scholar 

  12. W.C. Schefler, Statistics: Concepts and Applications (Benjamin/Cummings Publishing Company, San Francisco, 1988)

    Google Scholar 

  13. B. Scholkopf, A.J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (MIT Press, Cambridge, 2001)

    Google Scholar 

  14. R.M.M. Vallim, R.F. de Mello, Unsupervised change detection in data streams: an application in music analysis. Prog. Artif. Intell. 4(1), 1–10 (2015)

    Article  Google Scholar 

  15. V.N. Vapnik, Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing, Communications, and Control (Wiley, Hoboken, 1998)

    Google Scholar 

  16. V. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics (Springer, New York, 1999)

    MATH  Google Scholar 

  17. U. von Luxburg, B. Schölkopf, Statistical Learning Theory: Models, Concepts, and Results, vol. 10 (Elsevier North Holland, Amsterdam, 2011), pp. 651–706

    MATH  Google Scholar 

  18. T.J. Ypma, Historical development of the Newton-Raphson method. SIAM Rev. 37(4), 531–551 (1995)

    Article  MathSciNet  Google Scholar 

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Fernandes de Mello, R., Antonelli Ponti, M. (2018). A Brief Review on Machine Learning. In: Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-94989-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-94989-5_1

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