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Composing fisher kernels from deep neural models : a practitioner's approach

Author: Tayyaba Azim; Sarah Ahmed
Publisher: Cham, Switzerland : Springer, [2018] ©2018
Series: SpringerBriefs in computer science.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models.

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Azim, Tayyaba.
Composing fisher kernels from deep neural models.
Cham, Switzerland : Springer, [2018]
(OCoLC)1043851548
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tayyaba Azim; Sarah Ahmed
ISBN: 9783319985244 3319985248 9783319985251 3319985256
OCLC Number: 1050163130
Description: 1 online resource.
Contents: Intro; Preface; Acknowledgements; Contents; Acronyms; 1 Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges; 1.1 Kernel Learning Framework; 1.1.1 Kernel Definition; 1.2 Characteristics of Kernel Functions; 1.3 Kernel Trick; 1.4 Types of Kernel Functions; 1.5 Challenges Faced by Kernel Methods and Recent Advances in Large-Scale Kernel Methods; References; 2 Fundamentals of Fisher Kernels; 2.1 Introduction; 2.2 The Fisher Kernel; 2.2.1 Fisher Vector Normalisation; 2.2.2 Properties of Fisher Kernels; 2.2.3 Applications of Fisher Kernels. 2.2.4 Illustration of Fisher Kernel Extraction from Multivariate Gaussian Model2.2.5 Illustration of Fisher Kernel Derived from Gaussian Mixture Model (GMM); References; 3 Training Deep Models and Deriving Fisher Kernels: A Step Wise Approach; 3.1 How to Train Deep Models?; 3.1.1 Data Preprocessing; 3.1.2 Selection of an Activation Function; 3.1.3 Selecting the Number of Hidden Layers and Hidden Units; 3.1.4 Initializing Weights of Deep models; 3.1.5 Learning Rate; 3.1.6 The Size of Mini-Batch and Stochastic Learning; 3.1.7 Regularisation Parameter. 3.1.8 Number of Iterations of Gradient Based Algorithms3.1.9 Parameter Tuning: Evade Grid Search-Embrace Random Search; 3.2 Constructing Fisher Kernels from Deep Models; 3.2.1 Demonstration of Fisher Kernel Extraction from Restricted Boltzmann Machine (RBM); 3.2.2 MATLAB Implementation of Fisher Kernel Derived from Restricted Boltzmann Machine (RBM); 3.2.3 Illustration of Fisher Kernel Extraction from Deep Boltzmann Machine; 3.2.4 MATLAB Implementation of Fisher Kernel Derived from Deep Boltzmann Machine (DBM); References; 4 Large Scale Image Retrieval and Its Challenges. 4.1 Condensing Deep Fisher Vectors: To Choose or to Compress?4.2 How to Detect Multi-collinearity?; 4.2.1 Variance Inflation Factor (VIF); 4.3 Feature Compression Methods; 4.3.1 Linear Feature Compression Methods; 4.3.2 Non-linear Feature Compression Methods; 4.4 Feature Selection Methods; 4.4.1 Feature Selection via Filter Methods; 4.4.2 Feature Selection via Wrapper Methods; 4.4.3 Feature Selection via Embedded Methods; 4.5 Hands on Fisher Vector Condensation for Large Scale Data Retrieval; 4.5.1 Minimum Redundancy and Maximum Relevance (MRMR); 4.5.2 Parametric t-SNE; References. 5 Open Source Knowledge Base for Machine Learning Practitioners5.1 Benchmark Data Sets; 5.2 Standard Toolboxes and Frameworks: A Comparative Review; References.
Series Title: SpringerBriefs in computer science.
Responsibility: Tayyaba Azim, Sarah Ahmed.

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