Composing Fisher Kernels from Deep Neural Models

A Practitioner's Approach

AvTayyaba Azim,Sarah Ahmed

Häftad, Engelska, 2018

Del i serien SpringerBriefs in Computer Science

534 kr

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Beskrivning

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

Produktinformation

Utgivningsdatum:2018-09-05
Mått:155 x 235 x 5 mm
Vikt:147 g
Format:Häftad
Språk:Engelska
Serie:SpringerBriefs in Computer Science
Antal sidor:59
Förlag:Springer International Publishing AG
ISBN:9783319985237

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Mer om författaren

Dr. Tayyaba Azim is an Assistant Professor at the Center for Information Technology, Institute of Management Sciences, Peshawar, Pakistan. Sarah Ahmed is a current research student enrolled in Masters of Computer Science program at Institute of Management Sciences Peshawar, Pakistan. She has received her Bachelor’s Degree in Computer Science from Edwardes College, Peshawar,Pakistan. Her areas of interest include: Machine Learning, Computer Vision and Data-Science. Currently, her research work is centered around the feature compression and selection approaches for Fisher vectors derived from deep neural models. Her research paper: "Compression techniques for Deep Fisher Vectors" was awarded the best paper in the area of applications at ICPRAM conference 2017.

Innehållsförteckning

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.