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2190 results in Pattern Recognition and Machine Learning

Page 51 of 110

  • 10 - Information Theory and Decision Trees

  • from Part III - Classifiers and Tools
  • Jianxin Wu, Nanjing University, China
  • Book:
    Essentials of Pattern Recognition
    Published online:
    08 December 2020
    Print publication:
    19 November 2020, pp 219-242

  • Summary

    Information theory is developed in the communications community, but it turns out to be very useful for pattern recognition. In this chapter, we start with an example to develop the ideas of uncertainty and its measurement, i.e., entropy. A few core results in information theory are introduced: entropy, joint and conditional entropy, mutual information, and their relationships. We then move to differential entropy for continuous random variables and find distributions with maximum entropy under certain constraints, which are useful for pattern recognition. Finally, we introduce the applications of information theory in our context: maximum entropy learning, minimum cross entropy, feature selection, and decision trees (a widely used family of models for pattern recognition and machine learning).

Data-Driven Computational Neuroscience
  • Machine Learning and Statistical Models
  • Concha Bielza, Pedro Larrañaga
  • Published online:
    05 November 2020
    Print publication:
    26 November 2020
  • Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

Page 51 of 110