Abstract
Today we live in a data-rich environment. This is dramatically different from the last century when the fundamentals of machine learning , control theory and related subjects were established. Nowadays, vast and exponentially increasing data sets and streams which are often non-linear , non-stationary and increasingly more multi-modal /heterogeneous (combining various physical variables, signals with images/videos as well as text) are being generated, transmitted and recorded as a result of our everyday live. This is drastically different from the reality when the fundamental results of the probability theory , statistics and statistical learning where developed few centuries ago.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
ALMMo-1 is using a single user-controlled parameter (\( \varOmega_{0} \)) which, however, has very little influence on the result and represents the standard for recursive least squares (RLS) algorithm initialization of the covariance matrix [28, 29]. Its value can easily be fixed to, for example, \( \varOmega_{0} = 10 \). This algorithm can optionally also use another two user-defined parameters (\( \eta_{o} \) and \( \varphi_{0} \)) which control the quality of the generated model.
- 2.
ALMMo-0 and the DRB classifiers use a single user-controlled parameter (\( r_{o} \)) which, however, has very little influence on the result and represents the initial radius of the area of influence of the new data cloud. Its value can easily be fixed to, \( r_{o} = \sqrt {2\left( {1 - \cos \left( {30^{o} } \right)} \right)} \). Moreover, it is only required if the ALMMo-0 and the DRB classifiers work online.
- 3.
SS_DRB classifier only requires two such parameters (\( \varOmega_{1} \) and \( \varOmega_{2} \)) but they carry clear meaning and suggested value ranges are provided.
References
G.E. Moore, Cramming more components onto integrated circuits. Proc. IEEE 86(1), 82–85 (1998)
A. Oliva, A. Torralba, Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
N. Dalal, B. Triggs, in Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
K. Graumanand, T. Darrell, in The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. International Conference on Computer Vision, pp. 1458–1465 (2005)
G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, AID: a benchmark dataset for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965–3981 (2017)
K. Simonyan, A. Zisserman, in Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations, pp. 1–14 (2015)
A. Krizhevsky, I. Sutskever, G.E. Hinton, in ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, C. Hill, A. Arbor, in Going Deeper with Convolutions. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
A.K. Shackelford, C.H. Davis, A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 41(10), 2354–2363 (2003)
J.B. MacQueen, in Some Methods for Classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 233, pp. 281–297 (1967)
https://www.darpa.mil/news-events/lifelong-learning-machines-proposers-day
P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real Time (Wiley, 2012)
P.P. Angelov, D.P. Filev, An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern.—Part B Cybern. 34(1), 484–498 (2004)
E. Lughofer, P. Angelov, Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl. Soft Comput. 11(2), 2057–2068 (2011)
J. Macías‐Hernández, P. Angelov, Applications of evolving intelligent systems to oil and gas industry. Evol. Intell. Syst. Methodol. Appl., 401–421 (2010)
R. Ramezani, P. Angelov, X. Zhou, in A Fast Approach to Novelty Detection in Video Streams Using Recursive Density Estimation. International IEEE Conference Intelligent Systems, pp. 14-2–14-7 (2008)
P. Angelov, R. Yager, A new type of simplified fuzzy rule-based system. Int. J. Gen. Syst. 41(2), 163–185 (2011)
A. Corduneanu, C.M. Bishop, in Variational Bayesian Model Selection for Mixture Distributions. Proceedings of Eighth International Conference on Artificial Intelligence and Statistics, pp. 27–34 (2001)
N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cambridge University Press, Cambridge, 2000)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nat. Methods 13(1), 35 (2015)
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Crambridge, MA, 2016)
S.C. Prasad, P. Prasad, Deep Recurrent Neural Networks for Time Series Prediction, vol. 95070, pp. 1–54 (2014)
P.P. Angelov, X. Gu, Towards anthropomorphic machine learning. IEEE Comput. (2018)
S.L. Chiu, Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)
T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern. 15(1), 116–132 (1985)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Angelov, P.P., Gu, X. (2019). Introduction. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-02384-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02383-6
Online ISBN: 978-3-030-02384-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)