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Composing Fisher Kernels from Deep Neural Models A Practitioner's Approach

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fisher vectors using feature selection and compression technique...

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Bibliographic Details
Main Authors: Azim, Tayyaba, Ahmed, Sarah (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2018, 2018
Edition:1st ed. 2018
Series:SpringerBriefs in Computer Science
Subjects:
Mathematical Statistics
Computer Science / Mathematics
Probability And Statistics In Computer Science
Artificial Intelligence
Information Storage And Retrieval
Information Storage And Retrieval Systems
Signal, Speech And Image Processing
Information Retrieval
Signal Processing
Data Storage Representation
Automated Pattern Recognition
Computer Architecture
Pattern Recognition Systems
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 1. Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges
  • Chapter 2. Fundamentals of Fisher Kernels
  • Chapter 3. Training Deep Models and Deriving Fisher Kernels: A Step Wise Approach
  • Chapter 4. Large Scale Image Retrieval and Its Challenges
  • Chapter 5. Open Source Knowledge Base for Machine Learning Practitioners

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