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2190 results in Pattern Recognition and Machine Learning

Page 50 of 110

  • 1 - Introduction

  • from Part I - Introduction and Overview
  • Jianxin Wu, Nanjing University, China
  • Book:
    Essentials of Pattern Recognition
    Published online:
    08 December 2020
    Print publication:
    19 November 2020, pp 3-14

  • Summary

    This chapter is an overall introduction to the definition of pattern recognition, its relationship with machine learning and other relevant subject areas, and the main components and development process inside a pattern recognition system. This introduction is started by considering an autonomous driving example.

  • 14 - The Basic Idea behind Expectation-Maximization

  • from Part V - Advanced Topics
  • Jianxin Wu, Nanjing University, China
  • Book:
    Essentials of Pattern Recognition
    Published online:
    08 December 2020
    Print publication:
    19 November 2020, pp 316-332

  • Summary

    Parameter estimation is generally difficult, requiring advanced methods such as the expectation-maximization (EM). This chapter focuses on the ideas behind EM, rather than its complex mathematical properties or proofs. We use the Gaussian mixture model (GMM) as an illustrative example to find what leads us to the EM algorithms, e.g., complete and incomplete data likelihood, concave and nonconcave loss functions, and observed and hidden variables. We then derive the EM algorithm in general and its application to GMM.

  • 3 - Overview of a Pattern Recognition System

  • from Part I - Introduction and Overview
  • Jianxin Wu, Nanjing University, China
  • Book:
    Essentials of Pattern Recognition
    Published online:
    08 December 2020
    Print publication:
    19 November 2020, pp 44-62

  • Summary

    This chapter presents a simple but working face recognition system, which is based on the nearest neighbor search algorithm. Albeit simple, it is a complete pattern recognition pipeline. We can then examine every component in it, and analyze potential difficulties and pitfalls one may encounter. Furthermore, we introduce a problem-solving framework, which will be useful in the rest of this book and in solving other tasks.

  • 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.

Page 50 of 110