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2275 results in Pattern Recognition and Machine Learning

Page 55 of 114

  • 9 - Distance Metrics and Data Transformations

  • 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 196-218

  • Summary

    This chapter is not about one particular method (or a family of methods). Instead, it provides a set of tools useful for better pattern recognition, especially for real-world applications. They include the definition of distance metrics, vector norms, a brief introduction to the idea of distance metric learning, and power mean kernels (which is a family of useful metrics). We also establish by examples that proper normalizations of our data are essential, and introduce a few data normalization and transformation methods.

  • 7 - Classifiers and Tools

  • 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 143-172

  • Summary

    Starting from this chapter, Part III introduces several commonly used algorithms in pattern recognition and machine learning. Support vector machines (SVM) starts from a simple and beautiful idea: large margin. We first show that in order to find such an idea, we may need to simplify our problem setup by assuming a linearly separable binary one. Then we visualize and calculate the margin to reach the SVM formulation, which is complex and difficult to optimize. We practice the simplification procedure again until the formulation becomes viable, briefly mention the primal--dual relationship, but do not go into details of its optimization. We show that the simplification assumptions (linear, separable, and binary) can be relaxed such that SVM will solve more difficult tasks---and the key ideas here are also useful in other tasks: slack variables and kernel methods.

  • 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 55 of 114