Optimum-Path Forest
eBook - ePub

Optimum-Path Forest

Theory, Algorithms, and Applications

  1. 244 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Optimum-Path Forest

Theory, Algorithms, and Applications

About this book

The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions. - Presents the first book on Optimum-path Forest - Shows how it can be used with Deep Learning - Gives a wide range of applications - Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)

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Yes, you can access Optimum-Path Forest by Alexandre Xavier Falcao,João Paulo Papa in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Optimum-Path Forest
  2. Chapter 1 Introduction
  3. Chapter 2 Theoretical background and related works
  4. Chapter 3 Real-time application of OPF-based classifier in Snort IDS
  5. Chapter 4 Optimum-path forest and active learning approaches for content-based medical image retrieval
  6. Chapter 5 Hybrid and modified OPFs for intrusion detection systems and large-scale problems
  7. Chapter 6 Detecting atherosclerotic plaque calcifications of the carotid artery through optimum-path forest
  8. Chapter 7 Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest☆☆ The authors appreciate São Paulo Research Foundation (FAPESP) grants #2013/07375-0, #2014/12236-1, #2017/25908-6, #2018/15597-6, #2018/21934-5 and #2019/02205-5, and CNPq grants 307066/2017-7 and 427968/2018-6.
  9. Chapter 8 An iterative optimum-path forest framework for clustering
  10. Chapter 9 Future trends in optimum-path forest classification
  11. Index