| Document Type
|
:
|
BL
|
| Record Number
|
:
|
859537
|
| Main Entry
|
:
|
Naik, Azad
|
| Title & Author
|
:
|
Large scale hierarchical classification : : state of the art /\ Azad Naik, Huzefa Rangwala.
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer,, 2018.
|
| Series Statement
|
:
|
SpringerBriefs in computer science,
|
| Page. NO
|
:
|
1 online resource (xvi, 93 pages) :: illustrations (some color).
|
| ISBN
|
:
|
303001620X
|
|
|
:
|
: 3030016218
|
|
|
:
|
: 9783030016203
|
|
|
:
|
: 9783030016210
|
|
|
:
|
3030016196
|
|
|
:
|
9783030016197
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references.
|
| Contents
|
:
|
Introduction -- Background -- Hierarchical structure inconsistencies -- Large-scale hierarchical classification with feature selection -- Multi-task learning -- Conclusions and future research directions.
|
| Abstract
|
:
|
This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy; 2. Incorporating relationships during model learning leads to optimization issues; 3. Feature selection; 4. Scalability due to large number of examples, features and classes; 5. Hierarchical inconsistencies; 6. Error propagation due to multiple decisions involved in making predictions for top-down methods. The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks.
|
| Subject
|
:
|
Supervised learning (Machine learning)
|
| Subject
|
:
|
Artificial intelligence.
|
| Subject
|
:
|
Computers-- Database Management-- Data Mining.
|
| Subject
|
:
|
Computers-- Intelligence (AI) Semantics.
|
| Subject
|
:
|
Data mining.
|
| Subject
|
:
|
Supervised learning (Machine learning)
|
| Subject
|
:
|
Data Mining and Knowledge Discovery.
|
| Subject
|
:
|
Artificial Intelligence (incl. Robotics).
|
| Dewey Classification
|
:
|
006.3/1
|
| LC Classification
|
:
|
Q325.75
|
| Added Entry
|
:
|
Rangwala, Huzefa
|