عنوان

Large scale hierarchical classification :

(Number (ISBN

303001620X

(Number (ISBN

3030016218

(Number (ISBN

9783030016203

(Number (ISBN

9783030016210

Erroneous ISBN

3030016196

Erroneous ISBN

9783030016197

Title Proper

Large scale hierarchical classification :

General Material Designation

[Book]

Other Title Information

state of the art /

First Statement of Responsibility

Azad Naik, Huzefa Rangwala.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2018.

Specific Material Designation and Extent of Item

1 online resource (xvi, 93 pages) :

Other Physical Details

illustrations (some color).

Series Title

SpringerBriefs in computer science,

ISSN of Series

2191-5768

Text of Note

Includes bibliographical references.

Text of Note

Introduction -- Background -- Hierarchical structure inconsistencies -- Large-scale hierarchical classification with feature selection -- Multi-task learning -- Conclusions and future research directions.

0

Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030016203

International Standard Book Number

9783030016197

Supervised learning (Machine learning)

Artificial Intelligence (incl. Robotics).

Data Mining and Knowledge Discovery.

Artificial intelligence.

Computers-- Database Management-- Data Mining.

Computers-- Intelligence (AI) & Semantics.

Data mining.

Supervised learning (Machine learning)

COM021030

UNF

UNF

UYQE

Number

Edition

Class number

Naik, Azad

Rangwala, Huzefa

Date of Transaction

20200823073240.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y